Association of CAC susceptibility loci plaque characteristics.

A ‘druggable-MI-targets’ project

Setup

We will clean the environment, setup the locations, define colors, and create a datestamp.

Clean the environment.

Set locations and working directories…

Create a new analysis directory...
[1] FALSE
[1] FALSE
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[1] "/Users/slaan3/OneDrive - UMC Utrecht/PLINK/analyses/lookups/AE_20200512_COL_MKAVOUSI_MBOS_CHARGE_1000G_CAC"
 [1] "20200630_zipped_results"                               
 [2] "20200630_zipped_results.zip"                           
 [3] "AE_20200512_COL_MKAVOUSI_MBOS_CHARGE_1000G_CAC.nb.html"
 [4] "AE_20200512_COL_MKAVOUSI_MBOS_CHARGE_1000G_CAC.Rmd"    
 [5] "AE_20200512_COL_MKAVOUSI_MBOS_CHARGE_1000G_CAC.Rproj"  
 [6] "bulkRNAseq"                                            
 [7] "CAC"                                                   
 [8] "LICENSE"                                               
 [9] "README.html"                                           
[10] "README.md"                                             
[11] "scRNAseq"                                              
[12] "SNP"                                                   

… a package-installation function …

install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if (isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    #update.packages(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"http://cran-mirror.cs.uu.nl/\")", x)))
  }
  if (isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    source("http://bioconductor.org/biocLite.R")
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented.
    #biocLite(character(), ask = FALSE) 
    eval(parse(text = sprintf("biocLite(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}

… and load those packages.

install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")

install.packages.auto("haven")
install.packages.auto("tableone")

install.packages.auto("BlandAltmanLeh")

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")

install.packages.auto("ggpubr")

install.packages.auto("UpSetR")

devtools::install_github("thomasp85/patchwork")

We will create a datestamp and define the Utrecht Science Park Colour Scheme.

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
### No. Color                 HEX   (RGB)                                     CHR         MAF/INFO
###---------------------------------------------------------------------------------------
### 1     yellow                #FBB820 (251,184,32)                      =>    1       or 1.0>INFO
### 2     gold                #F59D10 (245,157,16)                    =>    2       
### 3     salmon                #E55738 (229,87,56)                   =>    3       or 0.05<MAF<0.2 or 0.4<INFO<0.6
### 4     darkpink          #DB003F ((219,0,63)                   =>    4       
### 5     lightpink         #E35493 (227,84,147)                      =>    5       or 0.8<INFO<1.0
### 6     pink                #D5267B (213,38,123)                    =>    6       
### 7     hardpink          #CC0071 (204,0,113)                   =>    7       
### 8     lightpurple       #A8448A (168,68,138)                      =>    8       
### 9     purple                #9A3480 (154,52,128)                      =>    9       
### 10  lavendel            #8D5B9A (141,91,154)                      =>    10      
### 11  bluepurple        #705296 (112,82,150)                    =>    11      
### 12  purpleblue        #686AA9 (104,106,169)               =>    12      
### 13  lightpurpleblue #6173AD (97,115,173/101,120,180)    =>  13      
### 14  seablue             #4C81BF (76,129,191)                      =>    14      
### 15  skyblue             #2F8BC9 (47,139,201)                      =>    15      
### 16  azurblue            #1290D9 (18,144,217)                      =>    16      or 0.01<MAF<0.05 or 0.2<INFO<0.4
### 17  lightazurblue     #1396D8 (19,150,216)                    =>    17      
### 18  greenblue           #15A6C1 (21,166,193)                      =>    18      
### 19  seaweedgreen      #5EB17F (94,177,127)                    =>    19      
### 20  yellowgreen       #86B833 (134,184,51)                    =>    20      
### 21  lightmossgreen  #C5D220 (197,210,32)                      =>    21      
### 22  mossgreen           #9FC228 (159,194,40)                      =>    22      or MAF>0.20 or 0.6<INFO<0.8
### 23  lightgreen      #78B113 (120,177,19)                      =>    23/X
### 24  green                 #49A01D (73,160,29)                     =>    24/Y
### 25  grey                  #595A5C (89,90,92)                        =>  25/XY   or MAF<0.01 or 0.0<INFO<0.2
### 26  lightgrey           #A2A3A4 (162,163,164)                 =>    26/MT
###
### ADDITIONAL COLORS
### 27  midgrey         #D7D8D7
### 28  verylightgrey   #ECECEC"
### 29  white           #FFFFFF
### 30  black           #000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------

Introduction

Project details

Collaborators

Maryam Kavousi, Patricia Peyser, Maxime Bos

Project ID AE_20200512_COL_MKAVOUSI_MBOS_CHARGE_1000G_CAC

Background

Here we map the CHARGE Consortium 1000G GWAS on coronary artery calcification (CAC) susceptibility loci to the single-cell carotid plaque data. These are given in:

• IndSigSNPsforSander.xlsx
• 20210127_Overview_Genes_MMBos_Sander.xlsx

library(openxlsx)

# old list
# gene_list <- read.xlsx(paste0(TARGET_loc, "/GeneList_15042020.xlsx"))

# update list
gene_list <- read.xlsx(paste0(TARGET_loc, "/20210127_Overview_Genes_MMBos_Sander.xlsx"))

variant_list <- read.xlsx(paste0(TARGET_loc, "/CandidateSNPsforSander.xlsx"))

DT::datatable(gene_list)

DT::datatable(variant_list)

NA

We will construct a list of genes to map to our scRNAseq data.

target_genes <- unlist(gene_list$symbol)
target_genes

 [1] "AC011294.3"   "ADAMTS7"      "ADAMTS9"      "ADAMTS9-AS1"  "ADAMTS9-AS2"  "ADK"          "AL137026.1"  
 [8] "AP3M1"        "APOC1"        "APOE"         "ARID5B"       "ATP2B1"       "BCAM"         "BCAR1"       
[15] "C6orf195"     "C9orf53"      "CAMK2G"       "CDKN2A"       "CDKN2B"       "CHRNA5"       "CHRNB4"      
[22] "COL4A1"       "COL4A2"       "COMT"         "COMTD1"       "CTSH"         "DUPD1"        "DUSP13"      
[29] "EDN1"         "ENPP1"        "ENPP3"        "ESYT3"        "FGF23"        "FHL5"         "GFOD1"       
[36] "GGCX"         "HELLS"        "HHIPL1"       "IGFBP3"       "KAT6B"        "KIAA1462"     "KRT8P46"     
[43] "LINC00472"    "LPGAT1"       "LRRC37A15P"   "MAT2A"        "MORF4L1"      "MRAS"         "MTAP"        
[50] "PDGFD"        "PHACTR1"      "PRKAR2B"      "PVRL2"        "RP11-145E5.5" "RP1-257A7.4"  "RP1-257A7.5" 
[57] "RTKN2"        "SAMD8"        "SMG6"         "TBC1D7"       "TOMM40"       "VCL"          "VDAC2"       
[64] "ZFHX4"        "ZNF32"        "ZNF485"      

We will test the hypothesis that CAC susceptibility loci are associated with plaque characteristics. We will use data from the Athero-Express Biobank Study.

Study design

We will perform regression analyses adjusted for age, sex (where applicable) and principal components.

Modeling

We will apply the following models:

model 1: phenotype ~ age + sex + chip-used + PC1 + PC2 + year-of-surgery

phenotypes are:

• calcification, coded Calc.bin no/minor vs. moderate/heavy staining
• collagen, coded Collagen.bin no/minor vs. moderate/heavy staining
• fat10, coded Fat.bin_10 no/<10% fat vs. >10% fat
• fat40, coded Fat.bin_40 no/<40% fat vs. >40% fat
• intraplaque hemorrhage, coded IPH.bin no vs. yes
• macrophages (CD68), coded macmean0 mean of computer-assisted calculation CD68⁺ region of interest
• smooth muscle cells (alpha-actin), coded smcmean0 mean of computer-assisted calculation SMA⁺ region of interest
• intraplaque vessel density (CD34), coded vessel_density manually counted CD34⁺ cells per 3-4 hotspots
• mast cells, coded Mast_cells_plaque manually counted mast cell tryptase⁺ cells (https://academic.oup.com/eurheartj/article/34/48/3699/484981)
• neutrophils (CD66b), coded neutrophils manually counted CD66b⁺ cells (https://pubmed.ncbi.nlm.nih.gov/20595650/)

Athero-Express Biobank Study

The Athero-Express Biobank Study (AE) contains plaque material of patients that underwent endarterectomyat two Dutch tertiary referral centers. Details of the study design were described before. Briefly, blood and plaque material were obtained during endarterectomy and stored at -80 ℃. Only carotid endarterectomy (CEA) patients were included in the present study. All patients provided informed consent and the study was approved by the medical ethics committee.

Athero-Express Genomics Study

DNA isolation and genotyping

We genotyped the AE in three separate, but consecutive experiments. In short, DNA was extracted from EDTA blood or (when no blood was available) plaque samples of 1,858 consecutive patients from the Athero-Express Biobank Study and genotyped in 3 batches.

For the Athero-Express Genomics Study 1 (AEGS1) 891 patients (602 males, 262 females, 27 unknown sex), included between 2002 and 2007, were genotyped (440,763 markers) using the Affymetrix Genome-Wide Human SNP Array 5.0 (SNP5) chip (Affymetrix Inc., Santa Clara, CA, USA) at Eurofins Genomics (formerly known as AROS).

For the Athero-Express Genomics Study 2 (AEGS2) 954 patients (640 makes, 313 females, 1 unknown sex), included between 2002 and 2013, were genotyped (587,351 markers) using the Affymetrix AxiomⓇ GW CEU 1 Array (AxM) at the Genome Analysis Center.

For the Athero-Express Genomics Study 3 (AEGS3) 658 patients (448 males, 203 females, 5 unknown sex), included between 2002 and 2016, were genotyped (693,931 markers) using the Illumina GSA MD v1 BeadArray (GSA) at Human Genomics Facility, HUGE-F.

All experiments were carried out according to OECD standards.

Genotyping calling

We used the genotyping calling algorithms as advised by Affymetrix (AEGS1 and AEGS2) and Illumina (AEGS3):

• AEGS1: BRLMM-P
• AEGS2: AxiomGT1
• AEGS3: Illumina GenomeStudio

Quality control after genotyping

After genotype calling, we adhered to community standard quality control and assurance (QCA) procedures of the genotype data from AEGS1, AEGS2, and AEGS3. Samples with low average genotype calling and sex discrepancies (compared to the clinical data available) were excluded. The data was further filtered on:

1. individual (sample) call rate > 97%,
2. SNP call rate > 97%,
3. minor allele frequencies (MAF) > 3%,
4. average heterozygosity rate ± 3.0 s.d.,
5. relatedness (pi-hat > 0.20),
6. Hardy–Weinberg Equilibrium (HWE p < 1.0×10^{−3<>), and}
7. Monomorphic SNPs (< 1.0×10^−6<>).

After QCA 2,493 samples remained, 108 of non-European descent/ancestry, and 156 related pairs. These comprise 890 samples and 407,712 SNPs in AEGS1, 869 samples and 534,508 SNPs in AEGS2, and 649954 samples and 534,508 SNPs in AEGS3 remained.

Imputation

Before phasing using SHAPEIT2, data was lifted to genome build b37 using the liftOver tool from UCSC (https://genome.ucsc.edu/cgi-bin/hgLiftOver). Finally, data was imputed with 1000G phase 3, version 5 and HRC release 1.1 as a reference using the Michigan Imputation Server. These results were further integrated using QCTOOL v2, where HRC imputed variants are given precendence over 1000G phase 3 imputed variants.

Quality control after imputation

We compared quality of the three AEGS datasets, and listed some variables of interest.

• sample type (EDTA blood or plaque)
• genotyping chip used
• reason for filtering

We checked the studytype (AE or not), and identity-by-descent (IBD) within and between datasets to aid in sample mixups, duplicate sample use, and relatedness. In addition, during genotyping quality control samples were identified that deviated from Hardy-Weinberg Equilibrium (HWE), had discordance in sex-coding and genotype sex, and deviated from the principal component analysis (PCA) plot.

We will load the Athero-Express Biobank Study data, and all the samples that were send for genotyping and the final QC’ed sampleList.

Loading data

Loading Athero-Express Biobank Study clinical and biobank data, as well as the SampleList of genetic data.

cat("* get Athero-Express Biobank Study Database...")

* get Athero-Express Biobank Study Database...

# METHOD 1: It seems this method gives loads of errors and warnings, which all are hard to comprehend
#           or debug. We expect 3,527 samples, and 927 variables; we get 927 variables!!!
# AEdata = as.data.table(read.spss(paste0(INP_loc,"/2017-1NEW_AtheroExpressDatabase_ScientificAE_20171306_v1.0.sav"),
#                                  trim.factor.names = TRUE, trim_values = TRUE, # we trim spaces in values
#                                  reencode = TRUE, # we re-encode to the local locale encoding
#                                  add.undeclared.levels = "append", # we do *not* want to convert to R-factors
#                                  use.value.labels = FALSE, # we do *not* convert variables with value labels into R factors
#                                  use.missings = TRUE, sub = "NA", # we will set every missing variable to NA
#                                  duplicated.value.labels = "condense", # we will condense duplicated value labels
#                                  to.data.frame = TRUE))
# AEdata.labels <- as.data.table(attr(AEdata, "variable.labels"))
# names(AEdata.labels) <- "Variable"

# METHOD 2: Using library("haven") importing seems flawless; best argument being:
#           we expect 3,527 samples and 888 variables, which is what you'd get with this method
#           So for now, METHOD 2 is prefered. 
#            
require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

# writing off the SPSS data to an Excel.
# fwrite(AEdata, file = paste0(INP_loc,"/2017-1NEW_AtheroExpressDatabase_ScientificAE_20171306_v1.0.values.xlsx"), 
#        sep = ";", na = "NA", dec = ".", col.names = TRUE, row.names = FALSE,
#        dateTimeAs = "ISO", showProgress = TRUE, verbose = TRUE)
# warnings()

AEDB[1:10, 1:10]

dim(AEDB)

[1] 3791 1091

cat("* get Athero-Express Genomics Study keys...")

* get Athero-Express Genomics Study keys...

AEGS123.sampleList.keytable <- fread(paste0(AEGSQC_loc, "/QC/SELECTIONS/20200319.QC.AEGS123.sampleList.keytable.txt"))

dim(AEGS123.sampleList.keytable)

[1] 2124   25

# AEGS123.sampleList.keytable[1:10, 1:10]

Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is.

There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/.

```r
AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, \/\, Today, \.AEDB.dictionary.html\)) 

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiRm9sbG93aW5nIDMgdmFyaWFibGVzIGhhdmUgb25seSBtaXNzaW5nIHZhbHVlcyBhbmQgYXJlIG5vdCBzaG93bjpcbnllYXJwc3k1IFszMjZdLCB5ZWFyY2hvbDMgWzM0N10sIHllYXJhYmxvMyBbNDE5XVxuIn0= -->

Following 3 variables have only missing values and are not shown: yearpsy5 [326], yearchol3 [347], yearablo3 [419]

<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



### Fix clinical data

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing   -999    
- Asymptomatic  0   
- TIA   1   
- minor stroke  2   
- Major stroke  3   
- Amaurosis fugax   4   
- Four vessel disease   5   
- Vertebrobasilary TIA  7   
- Retinal infarction    8   
- Symptomatic, but aspecific symtoms    9
- Contralateral symptomatic occlusion   10  
- retinal infarction    11  
- armclaudication due to occlusion subclavian artery, CEA needed for bypass 12  
- retinal infarction + TIAs 13  
- Ocular ischemic syndrome  14  
- ischemisch glaucoom   15  
- subclavian steal syndrome 16  
- TGA   17

We will group as follows:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin 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 -->

```r

# Fix symptoms

attach(AEDB)
AEDB[,"Symptoms.5G"] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"


# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")

              
               Asymptomatic Ocular Other Retinal infarction Stroke  TIA <NA>
  Asymptomatic          333      0     0                  0      0    0    0
  Symptomatic             0    416   119                 43    732 1045    0
  <NA>                    0      0     0                  0      0    0 1103

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

We will also fix the plaquephenotypes variable.

Coding of symptoms is as follows:

• missing -999
  
• not relevant -888
• fibrous 1
  
• fibroatheromatous 2
  
• atheromatous 3

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the diabetes status variable.

# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the smoking status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire.

• diet801: are you a smoker?
• diet802: did you smoke in the past?

We already have some variables indicating smoking status:

• SmokingReported: patient has reported to smoke.
• SmokingYearOR: smoking in the year of surgery?
• SmokerCurrent: currently smoking?

require(labelled)

Loading required package: labelled

AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")

Fixing smoking status.

attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")

* Current smoking status.

table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))

Current smoker
no data available/missing                        no                       yes                      <NA> 
                        0                      2364                      1308                       119 

cat("\n* Updated smoking status.\n")

* Updated smoking status.

table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))

Updated smoking status
Current smoker      Ex-smoker   Never smoked           <NA> 
          1308           1814            389            280 

cat("\n* Comparing to 'SmokerCurrent'.\n")

* Comparing to 'SmokerCurrent'.

table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))

                      Current smoker
Updated smoking status no data available/missing   no  yes <NA>
        Current smoker                         0    0 1308    0
        Ex-smoker                              0 1814    0    0
        Never smoked                           0  389    0    0
        <NA>                                   0  161    0  119

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the alcohol status variable.

# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix and inverse-rank normal transform the continuous (manually) scored plaque phenotypes.

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

AEDB$MAC_rankNorm <- qnorm((rank(AEDB$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$macmean0)))
AEDB$SMC_rankNorm <- qnorm((rank(AEDB$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$smcmean0)))
AEDB$Neutrophils_rankNorm <- qnorm((rank(AEDB$neutrophils, na.last = "keep") - 0.5) / sum(!is.na(AEDB$neutrophils)))
AEDB$MastCells_rankNorm <- qnorm((rank(AEDB$Mast_cells_plaque, na.last = "keep") - 0.5) / sum(!is.na(AEDB$Mast_cells_plaque)))
AEDB$VesselDensity_rankNorm <- qnorm((rank(AEDB$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB$vessel_density_averaged)))

```r
library(labelled)
AEDB$Gender <- to_factor(AEDB$Gender)
ggpubr::gghistogram(AEDB, \macmean0\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\, 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \% of macrophages (CD68)\,
                    xlab = \% per region of interest\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
ggpubr::gghistogram(AEDB, \MAC_rankNorm\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\, 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \% of macrophages (CD68)\,
                   xlab = \% per region of interest\ninverse-rank normalized number\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
ggpubr::gghistogram(AEDB, \smcmean0\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\, 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \% of smooth muscle cells (SMA)\,
                    xlab = \% per region of interest\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
ggpubr::gghistogram(AEDB, \SMC_rankNorm\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\, 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \% of smooth muscle cells (SMA)\,
                   xlab = \% per region of interest\ninverse-rank normalized number\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
ggpubr::gghistogram(AEDB, \neutrophils\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\, 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \number of neutrophils (CD66b)\,
                    xlab = \counts per plaque\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
ggpubr::gghistogram(AEDB, \Neutrophils_rankNorm\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\, 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \number of neutrophils (CD66b)\,
                   xlab = \counts per plaque\ninverse-rank normalized number\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

<img 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IcRQACBSQo8Vz5T7q1ZLPXhPvm3XWsmWQqrjScw948PiKe/X5oOXSnd8+eOl53lCCCAAAIIIIAAAgjkhQABmLwYJhqJAAK5INDv8kibt1Q8Es2F5hRsGzy9veLt6xd3KFSwfaRjCCCAAAIIIIAAAsUnwE+Qim/M6TECCCCAAAIIIIAAAggggAACCGRYgABMhsGpDgEEEEAAAQQQQAABBBBAAAEEik+AAEzxjTk9RgABBBBAAAEEEEAAAQQQQACBDAsQgMkwONUhgAACCCCAAAIIIIAAAggggEDxCRCAKb4xp8cIIIAAAggggAACCCCAAAIIIJBhAQIwGQanOgQQQAABBBBAAAEEEEAAAQQQKD4BAjDFN+b0GAEEEEAAAQQQQAABBBBAAAEEMizgzXB9VIcAAkagoqIChzwUWNHbKHMbO8QbjeRh6/OnyTuOP1YkEpFgVWX+NDpNLfV5+F4kTZQUgwACCCCAAAII5JwAAZicGxIahAACuSpQFQlKVSCYq80rmHb1N9QXTF/oCAIIIIAAAggggAACtgABGFuCRwQcFugPReS17T1WLaFQSKLRqLg9HvG40/eN93SH+0DxCCCQOQF7f+FUjUdEolLiVOGUiwACCCCAAAIIIDBCgADMCBJmIOCMgDnWkY7+2E9XgoFYAMZjXoEmBkNCAAEERgjY+4sRC9I0w8SASQgggAACCCCAAAIZFEjfV+8ZbDRVIYAAAggggAACCCCAAAIIIIAAAvkkQAAmn0aLtiKAAAIIIIAAAggggAACCCCAQF4KEIDJy2Gj0QgggAACCCCAAAIIIIAAAgggkE8CBGDyabRoKwIIIIAAAggggAACCCCAAAII5KUAAZi8HDYajQACCCCAAAIIIIAAAggggAAC+STAXZDyabRoKwIIZFXgzdIGebJitlRFAnLu7rVZbUshVz7tqWfFHQxI25J9pW8GN1cv5LGmbwgggAACCCCAQDEJEIApptGmrwggkJJAs6dMXi6fLg2h3pTKYeWxBcq3bRNvX790z50rfWNnZSkCCCCAAAIIIIAAAnkjwE+Q8maoaCgCCCCAAAIIIIAAAggggAACCOSrAAGYfB052o0AAggggAACCCCAAAIIIIAAAnkjQAAmb4aKhiKAAAIIIIAAAggggAACCCCAQL4KcA2YUUYuEolIZ2fnKEuzO9vr9UpFRYXViFA4LKFQyLEGRY2DndTD7S6MmF1lZaV4PJ6YoYN+tp39aI9VdGCGbmfRqP3MzpWGR1OmXVcaShtRRDhuu4iEI47WFY3s8dE+RcJ7nsc3zHJMsd/xJSfyi0TCVpVaV6Ll8e0ZdzquspTLGreyPRmSrituu0x6nT3VWNv1ZNazihiwCRvv0cqIuvbsi3Q/NVq+uCZNelK3cTuN1SY7z3iPYbPflrjX0JD8KboPKWucJ1HZsxE66RffDK2zry99V/bRfagmNR1eri4bPi++LemcDodG1p/O8r2BssHiAoGgBNJoOFjwwIRXt8+B1G/qiQad+4zhDQatmjLxmau0tFRKSkrMS8/57cJ+r+jvN9ey6u62OaW9vV1cLtfgcyYmL6Dvw11dXQXzuXTyEulZ03pfMkXpPtPer6an5NwoxX7950ZraEWxC+z5BFvsEvQfAQQQQAABBBBAAAEEEECgoAQIfBbUcOZ9ZzgDZpQh1DM9ampqRlmaO7O95iwOPSPGqeSKO+OlqqrK0bqc6sN45TrpN7xuu65gIGB996zbmX0mzvC8KT0337DZdaVUzigre+K2C7fH7WhdLveebwu1TxHXnm/t45un397omRCp9HtPTZKwHLc7dtaUvpGnUo/V7rjKUi4rHmKc6WTr0m/A7G/Bkl0nvuqUjAZsPMZ7tLo93j3fH+h+arR88W2a7LRu43Yaq012nrEeA2Y71dd8/L51SP64b8ed7JPW6TL/2cnpuux6tE79JjJdqaenxzr7RU317Ib4pPvXdNYVX/bwaY/X42hdbr9/sEq/3yfuNBoOFjww4R44O1Sflmg9HufOgPH5fFatmfzMlYntwn6vsLdJPRNGk36u5EDQokj5n97eXtGzme1tKOUCi7yAoDkbTd/zdZ+pn/dJCCDgnMCeT5XO1UHJCCCAAAIIIIAAAggggAACCCCAQFELOHfqRFGz0nkEEChEgcWB3XLW7rVSFnHuG+FCdJton1pWHCRuc72fvikNE12V/AgggAACCCCAAAII5KwAAZicHRoahgACuSYwK9gt+kdyVqBz70XOVkDpCCCAAAIIIIAAAghkQYCfIGUBnSoRQAABBBBAAAEEEEAAAQQQQKC4BAjAFNd401sEEEAAAQQQQAABBBBAAAEEEMiCAAGYLKBTJQIIIIAAAggggAACCCCAAAIIFJcAAZjiGm96iwACCCCAAAIIIIAAAggggAACWRAgAJMFdKpEAAEEEEAAAQQQQAABBBBAAIHiEiAAU1zjTW8RQAABBBBAAAEEEEAAAQQQQCALAgRgsoBOlQgggAACCCCAAAIIIIAAAgggUFwC3uLqLr1FYKiAyzX0Oc8QGEtgi69K3iptkLJIUD7QvW2srCxLQaDmrbfFFQ5Lz5xZEqitTaEkVi10Abc79j2S/Vjo/S20/rlK/IXWJas/Lr9vsF8u80HD7y/Mfg52kgkEEEAAgaQFCMAkTUXGQhTwuonAFOK4OtWnDf4a+UPt3tIQ6iUA4xSyKbfutdfF29cvjWVlBGAcdM5k0W6Hot2lpaWZ7AZ1pVnAO70+zSXmRnHeqXWDDdHgS0NDw+BzJhBAAAEEiluAnyAV9/jTewQQQAABBBBAAAEEEEAAAQQQyIAAZ8BkAJkqJiYQ2NoorT+/d2IrTTD3lEvOFW9t1QTXIjsCCCCAQCoC4WhUnt7QlUoRQ9YNBoMSjUbE4/GYv9hHmtOG5MjMk909IXkpjf0a3uragF+OGz6zgJ6H27vkvstudbRHJ/7HeVIxb7qjdQwvvGfNG9L6p79Jb2+vtaiyslL0J0npTNO/8i/i8nqk46GnpWfN6+ksekRZM/79X0fMYwYCCCCAwMQECMBMzIvcmRCImkrCkUzURB0IIIAAAhkW0F18upKWZWI6sb90FTrJctLZr+FNiIqTpQ+vLTvPIw6/70d1Q8l00jq1X3bf9DHNAZjBLplA5GA9gzOZQAABBBDINQF+gpRrI0J7EEAAAQQQQAABBBBAAAEEEECg4AQIwBTckNIhBBBAAAEEEEAAAQQQQAABBBDINQECMLk2IrQHAQQQQAABBBBAAAEEEEAAAQQKToAATMENKR1CAAEEEEAAAQQQQAABBBBAAIFcE+AivLk2IrQHAQRyVmB6qFsO694uVZFgzraxEBrWNX+euM3dbYJVlYXQHfqAAAIIIIAAAggggIAlQACGDQEBBBBIUmDf/t2ifyRnBZoPP9TZCigdAQQQQAABBBBAAIEsCPATpCygUyUCCCCAAAIIIIAAAggggAACCBSXAAGY4hpveosAAggggAACCCCAAAIIIIAAAlkQIACTBXSqRAABBBBAAAEEEEAAAQQQQACB4hIgAFNc401vEUAAAQQQQAABBBBAAAEEEEAgCwIEYLKATpUIIIAAAggggAACCCCAAAIIIFBcAgRgimu86S0CCCCAAAIIIIAAAggggAACCGRBgABMFtCpEgEEEEAAAQQQQAABBBBAAAEEikvAW1zdpbcIIIDA5AXaPCXS6C0XXzQiCwPtky+INccUKG3cJa5IRAK1NRIuKxszLwsRQAABBBBAAAEEEMgXAc6AyZeRop0IIJB1gVdLp8p/T1spP284MOttKeQGzPjbEzL7kUelfPvOQu4mfUMAAQQQQAABBBAoMgECMEU24HQXAQQQQAABBBBAAAEEEEAAAQQyL0AAJvPm1IgAAggggAACCCCAAAIIIIAAAkUmQACmyAac7iKAAAIIIIAAAggggAACCCCAQOYFCMBk3pwaEUAAAQQQQAABBBBAAAEEEECgyAQIwBTZgNNdBBBAAAEEEEAAAQQQQAABBBDIvAABmMybUyMCCCCAAAIIIIAAAggggAACCBSZAAGYIhtwuosAAggggAACCCCAAAIIIIAAApkX8Ga+SmpEAAEE8lOgPBqUacFuqQ/35WcH8qTVwaoqifj9EvH58qTFNBMBBBBAAAEEEEAAgfEFCMCMb0QOBBBAwBJY2dMo+kdyVmDbySc6WwGlI4AAAggggMCgQFlZmfjNFx/6R0IAAWcFCMA460vpCCCAAAIIIIBA/gi48qeptBQBBNIjUFFRkZ6CKAUBBMYVIAAzLhEZMi8Qle7+sKPVTok6WjyFI4AAAghkWSBq9vNOv5dkuYuOV98TCEvU/DmVKnkvdorW0XJDre0S7g86VofL4xH/tDrHyk9UcNTsMEKNLYkWpW2eu6JMPFWZDXRE+vol3NY5bh8ikYiVx+Vyif5NJHnqa8Tt5yfDEzEjb3ELEIAp7vHPyd6HzQeyl7f3Otq2eY6WTuEIIIAAAtkWCIQjjr+XLMx2Jx2u/82dvdLfF3KslmNDRGAcw3Ww4HV3PCpvPLfesRqmzK6Xo6/7Z8fKT1iw2V+0/Oh3CRela2b5Ecul+sRV6SouqXIC67dK212PjJu3p6dHwuGw9ROkkpKScfPHZ6j/1Kninz8zfhbTCCAwhgB3QRoDh0UIIIAAAggggAACCCCAAAIIIIBAOgQIwKRDkTIQQAABBBBAAAEEEEAAAQQQQACBMQQIwIyBwyIEEEAAAQQQQAABBBBAAAEEEEAgHQIEYNKhSBkIIIAAAggggAACCCCAAAIIIIDAGAJchHcMHBYhgAAC8QJhcUnQ5Ra3RMUfjd0xIH450+kRcAX1op9RiZo7YYib7wnSo0opCCCAAAIIIIAAAtkWIACT7RGgfgQQyBuBpypmyx31+0lDqFeu3vFk3rQ73xo6/557xWtundn4/lXSuWhBvjWf9iKAAAIIIIAAAgggkFCArxYTsjATAQQQQAABBBBAAAEEEEAAAQQQSJ8AAZj0WVISAggggAACCCCAAAIIIIAAAgggkFCAAExCFmYigAACCCCAAAIIIIAAAggggAAC6RMgAJM+S0pCAAEEEEAAAQQQQAABBBBAAAEEEgoQgEnIwkwEEEAAAQQQQAABBBBAAAEEEEAgfQIEYNJnSUkIIIAAAggggAACCCCAAAIIIIBAQgECMAlZmIkAAggggAACCCCAAAIIIIAAAgikT4AATPosKQkBBBBAAAEEEEAAAQQQQAABBBBIKOBNOJeZCCCAAAIjBN7fvU0O7dkhrhFLmJFOgU2nf1Qkav73etJZLGUhgAACCCCAAAIIIJBVAQIwWeWncgQQyCcBj4kKlEXD+dTkvGxr1OfLy3bTaAQQQAABBBBAAAEExhLgJ0hj6bAMAQQQQAABBBBAAAEEEEAAAQQQSIMAAZg0IFIEAggggAACCCCAAAIIIIAAAgggMJYAAZixdFiGAAIIIIAAAggggAACCCCAAAIIpEGgIK8B09jYKM8884xMnTpVli1bJlVVVWmgoggEEEAAAQQQQAABBBBAAAEEEEBgcgIFFYDp6+uTyy+/XF577TWprKyUrq4uK/hyww03yNKlSycnxFoIIIAAAggggAACCCCAAAIIIIBAigIF9ROkH//4x7Ju3Tq58cYb5cEHH5Q777xTpkyZYgVlOjs7U6RidQQQQAABBBBAAAEEEEAAAQQQQGByAgUTgNmwYYPcddddctJJJ8mhhx5qacycOVOuuOIK6e3tlfvvv39yQqyFAAIIIIAAAggggAACCCCAAAIIpChQMAGYNWvWSDQaleOOO24IiV4DpqGhQR577LEh83mCAAIITFTg+bLp8s0Zq+T7Uw+e6Krkn4DA7IcekXn3/kkqtmydwFpkRQABBBBAAAEEEEAgtwVcJmgRze0mJte666+/3jrL5Xe/+51Mnz59yEqf+cxnZOPGjfLnP/95yHx9smXLFrnssstGzD/ssMPkwgsvHDE/F2b4fD7rGjfals43N0gkFHasWf66KimbO90KbkUiEXG5XI7VpQW73W4J9/RJ1zpnD7yqlswXt98nobZO6d7c6GifqpcuFJfpV6CxRXobd8fVpS+99HpWH7jIGqO+zTulv60rrq70TnrLSqRi77lWod3vbJZQXyC9FcSVVjqlRkpmTbXmtL+6Lm5J+ifL500XX22VRAJB6Vy7aUQFt68LylUvBmROhUse/XD5iOUTmVFl/NzGMdzZI10btk9k1Qnnrd5/gbi8Hgm1tEv3tqYJrG+/PSS/ndrbYP/2Julrbp9AXXuybj720xJp7ZAp13xeKk85as+CYVM1yxZbc3o3bJNAZ++wpel76quukPK9ZloFdr21UcLBUAqFj/26L5vZIP6pdRI1+9uO199LoZ7xV61YMEu8VeUS6e2Xzne3jL9CCjkG97ntXdK9aWcKJSVadahp9QFmn+txS3BXq/TsbE20QtrmDW7vWxqlb7dzP3X2lJj3/X3nW+3uW79Fog7ucz111eIf2Od2vL7ebIv2fiBtbIMFlc+dJj5TX9S8pjrMa8vJVLl4jnjKSyXc1SOBTTucrEpKzVi5vF4JtbZLYIfZ5w4QOvEZqnQ//YzhkuDOZmsf72THyg5YZBXfu3G7BDp6HKsqfnvXz56Jkn34kg5T/eyp23nfW87uc70NteKb0WB9prbbn6hv6ZinLvoX7uiSgNk/jZeiupHqdmre7s2a42Ufsty/1yzxVJRZvzjQ63HmYiorK5PS0tJcbBptKkKBgrkIb3d3tzV8NTU1I4ZR74LU09MjuhPXnWx86ujokLvvvjt+ljWtO8bzzz9/xPxcm1FlDqwykXQn7vF4MlGV9QHJPrByukKvOdiuMX+ZSP7p5sDK/GUilc6bIaXzMlGTSMU+GarIdCdT24UG5xLVVdrXLPLiNit4l2j5ZMQ95gA4XWWNV7+3oUZqzF8mkgbN7MDZROtzm2CRfuQumzctKZuyBbOlbKKVTDJ/5X57TXLNia2mQdtMbRcaCMxUXd6ayozV5ZtWLzXmLxOpxHxRoX+ZSKWLYsHvTNRVvTR2wO10XS6fN2PbhaeyXOxAgtP98tbXiP5lIvlmTDEH91MyUZWUmQPuTO1zh39ud6qDGsTK1HZhB0ec6kt8uZ7qStOvyvhZjk2HQiHJ1QCM3+93rN8UjMBEBQomABMIBKxIb6Lopj1PdwzDX4D19fUJz3RZsWKFaLQ0F5N1lkjYubNeEvVZ3ywyEa3Xup2uR+uw3/y0LqfrS1RXfJ26PF3J/qCSiX7ZdY327VS6+qTlJFOXbZqq51h12d8Ea13hFF+DibaLdJrFlzWZumxPLWciprZfKtugfV6mbltjOdt15co2GG8+2rTtOpZppvo1me1itH6NN9+pumxPrd82daquRH20xyqV7T1RuYnm2f3K1PaeiT5pP23DTPTL3kbit5tE1qnOs+vRcuL7FT8/1Trs9e0yne6T1qdjlantwq4v3s/usz7a/bX7H79sotNahl3eRNedaH6tx3ac6LoTyT/RPsX3fzKm+l6t/crVYyevOSONhECuCBTM1qiBFN156Jku5eVDfxqgZ8fo2RvDgy86CHqh3ltvvTVXxiNn27Fjxw6ZOnWqFNoObDJvMpMdpPi6mpqaRAOCenaW3jI93Sm+rnSXPby8TJ0ZpfWOVZfedj4YDEpdXd3wJk7qeaK69BsyTeqbaPlkKsrkWE2kLv0Wa/fu2E/mdD850TSRuoaXbcck9cNcMs7J5Blex2Sfp1qX7kv17nzJ7EtTrSvZPqYyVsnWYedLd13Nzc3W6173o7o/jU/priu+7OHTmazL6e1C96X6pZZ+rspkcrpfmexLfF36vtTaGvsp3IwZMwYDhfF58mk6k9u6uoy2XezcudO6xmMy+9J88s1WW8falybTJh2nRMdZyaxLHgSKTWDo73HyuPf6gVaT/qRoeNJ5tbW1w2fzHAEEEEAAAQQQQAABBBBAAAEEEMiIQMEEYOxvaNevXz8Err+/37rQ7j777DNkPk8QQAABBBBAAAEEEEAAAQQQQACBTAkUTADmmGOOsX569NBDDw2x+/vf/25dlfuEE04YMp8nCCCAAAIIIIAAAggggAACCCCAQKYECiYAo9d9Of300+Xxxx+X73//+/L222/LAw88IN/61rfkfe97nxCAydQmRT0IIIAAAggggAACCCCAAAIIIDBcoGACMNqxCy+8UM4991y555575NOf/rTcdNNNctRRR8kXv/jF4f0u+Off+NC1Bd/HZDrY1dgm/Z29yWQdzLPugTWD06lOvPToG7Jjc1NKxbRsa5ENL2+YcBmtazdL4/PvTHi9V1/eKlu3xy6+OpGVN971qHTuaJzIKlbeJ+5dY52lNtqKHa1dEgqGBhf3tXZK18adg8/jJza/+F78U2s63G4uKLlt14j5m/7xtnXxTntBV3/Y3K0idnej3rdGlqP5Vi+pkf88yCvfOnmGvdqIx+ZX1lsXWLYXhPWClpt3WE/DXT16Swx7kfS0dcimZ98afK4Tunhnx57+try3p6+B7U0SMRdv1tTU3Cm9fQFrOmTKCXV0WdOBrY3WBcm7+mP1NDV1SXd3v7VM/wm8s2nInYXamjqkdVf74PJO4x0JRyzzno7Ya8eeFx6oo73J5Nc7QXV2S8RcrDO4q9U8BiXU0yvqaKdNa2P9tp+rg3rouhGzrpanF0/f3hHrh+Zr27hL5t1xvSx85H/EddgyCfYHJRLn1rGpUXQbsJPeeaH1vVg9kb5+iZp27O4JSU8g1n/10NSp42vKiZo8mi9i6tY/TSHjao9LcGezhFv3eOjyre82DhlTexzsdnW1dQ9uo3Z9ul6vaWd3a+y6ZJHuXssq2tNnjaGWoclql3EL9wcsu27TTt3eNb/tGzHLNem+TPdpmtQ70ttnGVozzD+63dp3jbLWNRf/tOo1ZUXNdqPmvcGIBEIRq15dL9TaJiH1HUiBnU0SaIpdMNS8HKTbOPaZ/LqOjlfEeGvZ/eZ5f8iM48DYaD+iZplVn/qa/uj2rvOs7WZgzLUdYW2Ptl/zmT5EB/pr5TVlR/XuVwPbhrWuaZuWryncvmfs4607zfau25K2TdfXdljtMeWrlVWHmdZxttqsZQ2Mv91O7aP21SonbpmuY/kNjIO9nj0G2q7Ovlj74vPZbdDl9jp2/3Wevb1a0wPt1Wm7D/a0vl406VjoPkqTZTiwL1DrqPmzUlz/9LmOub1+VLeHgT5Yy3R8TH5N8X22Zui4mTbZKb5fOi9s2qHtiU92H+Pn6XTEXNBb9xEJ08B2MXxZorLiPePz2/bx83R6tPnxdsPXsfYRAybDlw03GLF8mP3w5dbzZPJoxoFtOWEZCWZafTJjNpFkv14nss7wvInGaXieiT63X+sTXS/V/Lov0td0saTBfWKxdJh+IpBDAi7zpht7N8+hRqXaFL27zJYtW2Tu3LlJ3Wki1fpybf3PHX+9vD5vbzlgy7vyP3/5clqal493Qdq9sVH+/C/fFk95qZz1x6uTcvjV2dfJcy0iB/gDctGD/5HUOqNluu+Hj8h/P7tbqvq65A93/vOQbMneBUmvYXTmp38nfW6v3HThclly5JIh5Yz2pGtXs5z3hfslZNa75eJlMueIA0fLOmT+00+vl/P/sEs8kbC8es1KsW/hPiRTgifP/et/ifz8bgmWlsqRnX9PkCPxrO+e9z0pXfOKdE6dJl98ypQxLG16a5v84o6XZa/p5fLJzx1rLX168RniMh/o933oB1K/dOHgGjdd8jN5bkdQjpxXLhfdeP7g/LeXn20djC164Afi32e+Nf/RK24R9y+M68EHykmP3CgbWvrld6/sliXTSmXZf94g3U+9LFP/78dl6uWfGCxHJ75746Pyk81umdrfKY//zylDlumTl6/5pTT9+B7xrzxAjr77Gmv5O4d9QsItbTLzu/8m/S+/Lf7F86Tu4ydby6454RvSJCVy9mkHyPsvjpV37SM7ZM3mbvn8kVPF9fCT8pc12+So5dPlyGXTpPG6n0rZyv2l61/Pk/932zsytcwlP774INn4f74sLnPHoMoPHi7tf3hMXjntY/LeshVyWG1EXr72V+KqqZL/+7PPyq7v/VqazV/FEctl/m/+UzS4ddVnbzO3dRK5+keflF1bW+S2u16VhTMrTFAsIBsbu+S4/aul47FXpa62VParccumPrds3dEhC2ZVy6xSc8D6yrsSauuUipX7yV+r5sn2ww6XMw+ZKnff+qj8ZUdE/s++ZfKpzx8nPc++KpvO+6p4ptTJ9K/8s3Q89LS4vB657+Bj5eFog5yxrFaWblwrd/3xTVk2t1KO+/QH5alv/koqKkpk+VSPlCxZIMH9Fst/X/kHKXNH5Mo//D/L8OF/u1Va1qyV/c4/QWY275BW8cnXp62S6jKPXN/8rLTf/Vd564yPycaps+So5/8utY07pHTfBdL11EuWWcOnTpWmm38rFe8/SBo+d5asP/Zfxe3zyuLnfyWesjK5+xdPyq0vtFuWV199inQ+8ow1DiV7zxf/nGnSUjdV7mz2y/yp5XJqaZu0/uoBqfnY8VL1Tx+Ve8+62rrjyYd+8FnpvuMhCWzYLv1TqsTX3iNBEwjbduEF8mLlTDnmqT9Lh7dM/nrkiebAVmTxhrVy4roXjI/XHLh3i3/eDJly8Tlyzye/JaHuPln90ysk9Ie/Sv/6LeKuqpCqY1ZK7+vrpfn7v7H6MeMbF8nuXz8goeY2cZX6zfCaW6yaA4t+czH6ew78gLSWVUtNqUfeXxOSmV+7VlzlZTL/7m9L1Gyn7xz5KePikb3X/FLufS8ob+/ql6BpVF1Xm3zs1b+LyxzER30+eWi/90nY3PXiQ68/JVvrZ8jfD3yfHP/SEzKro9kEPvTAPCous9zlMbetNQfrOtbRkDlANM9Fgwjmo4feVUw/gQzeXczvs4ISnspyE4ToEU9dlYR3d8ra5YfImpq5srJnp+z3/LNSdcL7pPeN9dL2mwfFe+LhsvGU1fLk1qgc6e2SJU8/Ib45062gmndavRVMc5WVWu2JmOCXd9Y0CZlAYMn+i6Tv9XVSumxv69E3f5bcufhw01eRc3evlfBLbxrL5dLzzKviXzTXCqCEW9ql4gMHS9dja6Ri1TLpe/M9q707zzlDHt7YLyvnVsiyxx6WkAk2VJ92rHTc86j45s4Q//yZ0vW3F6T6wx+QXrMP0CBf/SdPkd2m/W7T55pzTpTdP7vXanfFUQfL7l/dL6UHLJLSA/eWttsfkrKD9pXdR71f7nq5VfY1+6iTp0Wk5ZbfiW/mFKucpu/9xvKceul51uu/zwTi6v/plFgwcmD9GtOeZrOdh81rVbelkBlrqx7jUHvWCdLxwBPS8483pNZsu6EFM2Ptae+WKf/6MfFOb5CW/7nTrNNureupq5Y7X2qVzW0BOe+QBplZ7ZOuv78gXY+ukarVq4zNcuu1af+z5YKvS/+WnTL7+1+SMtOv+NRx3+PS8+JaqTUGpfvF9uf6Gut+6hWp/ujRUn7wflb2oAmit/zU7Fv3mmX1zS7j8XWd1v7ypCXVcuCsPXfAHG1+yAQXW2652+pTw4Vn2MVYj4EN26T1l3+02lF79olDlukTNQ817ZaGz54l3oaRN3Zou/NhUfu68z8iJQvnDK6v7+P2XZBKH39FAu9ukjozPtqX0VK72XZ6X3nHeq8oGXjfGi1vqLFFmm+9W/yzp0n9P582WrYR83ebvvZv3C4Nnz5dfOZ1MdHUbfbpnWY/XnnsSqk8euVEV0+Y/+VtPfLI2x3yvr0q5QMLx75DpH0XJJ/ZH6WaNICl+093aYno68jsuFMtMufXb/rvX1ufjaZecq71PpLqXZByvsM0EIEcEjCfhAov6S3pFixYUJTBFx3NzvLYm1ZHeVXhDe4EerTbfCuuH/hD5gCm13yQTCY1t8a+8dvdu+cMhGTWS5Rn7UsbJWLexDtLx/4QkWhde1779jbp8pRIyOWRd17cYM8e97Hxhfek21ci/eYAaOtz746b386w5lVzhoI5WAuYwM2WLbFv2u1lYz3qB2g9kvKasyEmkgLbm63sXnOr+ESpcUebOVaLSnO7+ebaJD3zwaXf6JqjpLZX3xuySlN7rO6dLXu+tdWzX/RPt4O+tXv8Au/Epl3bY2fGNHfHxrulJyzBgTMm4vPbFa3d1mm1p8OMSaLU+fZma3Zw4OwGfaIHwXpA2vv8m9aBqB6c2ak96hE9VN36Rmw9nb+9PfYN3KbdQWncETsTY1djh/S/t1WPaSW0rUk2bTNnBRmXpt6oBHY0m2+1zZkQ5ttsu827dsXOEtjwXkvsm//m3ebMBZPXHPRrCm5ptB53bWgSPVFGTy5o2tIiu0x96t3U1ict5qwUnW5Zv9P6ljy4OXYmTmDTdmvdfj2rx7QnsM2ccWOC3gFTZmvIbR2QtpgzULa0DfSjKTYePa+tsxw0GKUHMVHzTbx+o715d+zsnM27A7JzU+yskKbdvdJqgkHWmRBbYvWoW8tbW0RL7Yi4pb8j1see7S1We9rXbjJntwSk0ZQXDoREx7LLHFxoajJ+/vYO6e4xZ2U0t1sHnxGzX1C33lffsfKEzAFe/7tbrINW/TAe3hkbp407YmfJ7Og2nTXJHofAxm3WeO7a1moFEZra+60Ai+YJmno7tppx0bNAzFke7W9vtfoe3NEUe9wU61P7hh3iNWcE9XQHpXfXbukzA9EdCBt/s15Hj+UTNNuobr89xj9o9mM63WrK0wNp/dM+q03gnY1atRnbndZza1sx22HUBGz0jAo9gO0Sj/SZs5qauszZSiYI0rppl2lj0AQ5OqyzQvrXm3bqGUfmm+DAFnPA221eN+aMmfbesHS09UrA5bbGWQMyOoYRM5Z61kiP2QY19exosdbVg/uw2e7122TdLjQFTLs06XakZ2vo+qGBeq0zijSvbmO6Tdnb2kbjZFJzU2z/0LRr4Iwl09+QGpoUNq/XVvM60NRpxkLXD5r19DWn5Wj/tD3W69BsF0Gz3WpAKKCvJ5N0rMyGLn1mvPWsMT2zp3tgm+pfHxtj9dU/tR9cz3ha+xZTfrM5y0yT7kcG85mDeW2D1U+zria7HI2yadt07DTApmNj5TX5wsbFGjuzPVjTA+u1DOyjtA59DWkZWl7EjK++jnRb1rM3dJ61vinHPuvEer2Z17L1aM420vHW6Vg9dttiZz3a6+j4WXXoo0nBxlj/QwNnh2k7TJHSOtAuq96BtlorxP2j272WFTQmw5Ndn9WegYUh03dNdpnWtNmH6TjFz9P5TV2xfbe9D9d5mkabHzZnpOk4Di9H1xluovPik7bVWtcEohKlwfXVdpSk9er2p4GgsZLdPttnzLzanoHtYax8w5dZdWhbBsZ4+PLxnttttMdrvPzJLLfH0X5MZp105ImYM+t036evaT0zrdCTnv2iwe2o2Sfq+wgJAQQyK1CQAZjMElIbAggggAACCCCAAAIIIIAAAgggMLYAAZixfViKAAIIIIAAAggggAACCCCAAAIIpCxAACZlQgpAAAEEEEAAAQQQQAABBBBAAAEExhYgADO2D0sRQAABBBBAAAEEEEAAAQQQQACBlAUIwKRMSAEIIIAAAggggAACCCCAAAIIIIDA2AIEYMb2YSkCCCCAAAIIIIAAAggggAACCCCQsoA35RIoAAEEECgSgR5zi2AxYWu9dTTJOYHAhu3S9N1fmTur+pyrhJIRQAABBBBAAAEEEMiwAGfAZBic6hBAIH8FNP4S1eZb/+RvP3K95aHmNul+7HmJ9vbnelNpHwIIIIAAAggggAACSQsQgEmaiowIIIAAAggggAACCCCAAAIIIIDA5AQIwEzOjbUQQAABBBBAAAEEEEAAAQQQQACBpAUIwCRNRUYEEEAAAQQQQAABBBBAAAEEEEBgcgIEYCbnxloIIIAAAggggAACCCCAAAIIIIBA0gIEYJKmIiMCCCCAAAIIIIAAAggggAACCCAwOQECMJNzYy0EEEAAAQQQQAABBBBAAAEEEEAgaQECMElTkREBBBBAAAEEEEAAAQQQQAABBBCYnIB3cquxVi4LzGjdJZW9XVJu/tKVGhoaxOPxpKu4jJQzZd85MvdDh1t1lVaWJVXn0sMWSOXrW2XGvFlJ5R8r00FH7is773tZXBIdke3222+Xbdu2yerVq+W4444bsdye0TCrQY5y77ae7n/4Mnv2uI9TD5wnH7zlcSvfnMNPHje/nWHVitnyt/feEYlGZa+5U+3Z4z7Wf+QoaWvrEKlIztkucMaRy2XXGr+UTK+3Zw15nL94uhzbHZDycr81v6S6XHynHGNMXVJ30OIheQ8/ZI7M2NAiC/eZvme+GffqM44TjTT7D1g0OL/hFNPeykopXRgb53l1fjlqUZVU+N1Se+YHJbBhm5R/4ODB/PbEcQfPkOiLjVJZkjh2PePkw6W5slQq5u/ZfurOOkGiwaBUHneouL1ecZeV2MXJwXtVS19Pv+x39NLBeauXVEtLd0hWzCkX7/v3loZ3d8ishdOlYl6NSDgi3il1ss/Cevno9g7xetzinzlFas8+wVrfN3eGBJftLYfN2UtCpj+ls33yzoePEMMlbo9LKk86QtylfvHOm2nln7Zgqhy+NOY1dW6DlFWVyrF9QSkz3hs2rpd7H/iTtJb75MJTLxB/T49ULWyQafvullJfqdSG+qRqTp24q8rFXeIXz9RaOaJ6ugQX1ol6Hr98iize3ikH7DPFqqvi8KVSq2Nh8pabaW99tWmUW46vniH7VtbKoiklMqt0sXi8HqnYr0bmLz1Dtrf0ic9QV5n+us225S0rl6PXrBOX2yWe8ti2ttdJh0rXjhaZuWyBVFX5ZEFPRE6pqheX2V81zFklgUVzZMXchRKcNdM890lN40Lxm/Hx7zVTXH6flOy/ULzG0Dd9ipTsPddqozbYMyO2TR5htiufd5vMnGL8Tao4/EBrHFw+r5Tut1AWdAZEQmYbLvFKhbdBvLOnin/RXPHMrBvc/9QeuECktkz882aIe8EsCe+/t0T7+2XvpfvL9IZpMqVuldQEo3LCvrUSDEelrnyO1CwtEZfPJ6HmNikxffDNnTZYXr3Zt/kqPFK6dJF4aqrEN2eaeKorxF1eKv4Fs62/qg8eLiWL54mnrsp6PYc7uqVy9gw5umKKNJdUyvQqvzT0u6xtR/dQuq7XmOgYafKb+g6v8ci+00qkJxiRqs6oNOxzqIQPXCiuKfWyvLzBjINHapeUy+ywV8rN9taw+nCp9gQl0tsvEolY20a4vVu8DTUSam03Y24ed3eIp6pCIj294jKvB/PGYlm4dV5nj9WPsD6a7SrS3WuN+4Gl1TJ3apXU1M6Wqn0qxDujQdw1lZb1/77xD1n/m5tkv0OPkb0POUiqFldKNBy2xj9iXi9u8xqJmv2Z/rnN9iYu82Iw0+bFY8YtLGZwRYIhiRjro2YYK5Pq6s322bWXuMw2F9U2mNdspN+Mc8SUYeot2Vtdq8W/z3x9acli89qsDBiLMo9UHn9Y7HU6a6p4TR7dxjxmHd/sadafx1hovb6Fc6Tqg1Z1UmLGTMx4aV6f2UZ07NyV5vU/tS42bcZ2bq1pnzEu97nEV+a35us2qK8Lza9J21vx/oMsN//8mRI19bhMf91mfX2sOuF9Vt+1DS7TJ11P19FUfugBZnuZa9XvKSuT+tXvF49x8pnXhqaqE1dZY+qdUms9X7VXpQTMtjqj2mc9L122j/hmTLFeS9aMuH9qz15t/PqlNG4fbC/W11N4SZe1zdrzyg5eYl6jM812vWdfrn5We0ti9dl5l88us/Y3s2oSz5850D47v2dafawcHf9hSeu07AdMhi22luk+2GfKSJTKj1hubS9aTnzavHmz/OQnP7Fm/cc5F5ht3+zXBvbB8fnip8tXLZOIec36F5ptY5zkM68HaxvQbXkCqeLolea1FxCf2VYnk3Q89TXtHcVjMmXuPdVslyUeqS8fOT7Dy6uvrxev7kPSkDy1VZahtS8071EFn8z2X6mvf9NRt9lPabrrrrtk48aNcuyxx8pJJ51kzeMfBBBwRsBlPpTo/oaEAAIZFDjkkEPkxRdflOuuu06+8pWvZLBmqkIgOYEHHnhAPvzhD1uB11AolNxK5EIgwwKrVq2SZ599Vr7xjW/I17/+9QzXTnUIjC/wl7/8RU44IRYkj5jApAbDSAjkmsBRRx0lTzzxhHzta1+Ta665JteaR3sQKCiBxF/jFlQX6QwCCCCAAAIIIIAAAggggAACCCCQXQECMNn1p3YEEEAAAQQQQAABBBBAAAEEECgCAQIwRTDIdBEBBBBAAAEEEEAAAQQQQAABBLIrQAAmu/7UjgACCCCAAAIIIIAAAggggAACRSCQnsuHFwEUXUQgnQLf/va3zV2D2uSAAw5IZ7GUhUDaBPRC0b///e+5YGTaRCnICYHrr79eWltbZb/99nOieMpEIGWB5cuXW/tSLYgL8KbMSQEOCVx77bXS3NwsS5YscagGikUAAVuAuyDZEjwigAACCCCAAAIIIIAAAggggAACDgnwEySHYCkWAQQQQAABBBBAAAEEEEAAAQQQsAUIwNgSPCKAAAIIIIAAAggggAACCCCAAAIOCRCAcQiWYhFAAAEEEEAAAQQQQAABBBBAAAFbgIvw2hI8IpBFge3bt8vDDz8sF1xwQRZbQdUI7BFobGyUZ555RqZOnSrLli2TqqqqPQuZQiCHBNh/5tBg0JQhAj09PfL888+LbqPV1dXWxaIXLFgwJA9PEMi2wNq1a+XNN9+U0tJS6/1+zpw52W4S9SNQ0AJchLegh5fO5YOAfkD7zGc+Ixs3bpQnnngiH5pMGwtYoK+vTy6//HJ57bXXpLKyUrq6uqzgyw033CBLly4t4J7TtXwUYP+Zj6NWHG1es2aNXHPNNdZduvTAVvetmk499VS54ooruCNScWwGOd/L//qv/5I//elP4vF4xO12SzAYlNNPP10uvfRS63nOd4AGIpCHAvwEKQ8HjSYXjoCeZfClL33JCr4UTq/oST4L/PjHP5Z169bJjTfeKA8++KDceeedMmXKFCso09nZmc9do+0FJsD+s8AGtIC609LSIldddZX4fD65+uqr5YEHHpBf/vKXcuyxx8q9994rd911VwH1lq7kq8BLlrKE8wAALOZJREFUL71kBV/OPPNMueeee6xt87zzzrOmf/3rX+drt2g3AjkvQAAm54eIBhaqgL7ZnX/++fL222/LrFmzCrWb9CuPBDZs2GAdGJx00kly6KGHWi2fOXOm9W1tb2+v3H///XnUG5payALsPwt5dPO/b0899ZRowFrPJDjmmGOsQIz+9Oiyyy4Tr9crjzzySP53kh7kvcCPfvQjqampkYsuukjq6uqss10/9alPWT+X44zsvB9eOpDDAgRgcnhwaFphC/zwhz+UffbZR376059avwsv7N7Su3wQ0FPmo9GoHHfccUOaq9eAaWhokMcee2zIfJ4gkC0B9p/ZkqfeZATmzZsnn/zkJ2X16tVDstfW1loHuh0dHUPm8wSBbAhcf/31cvPNN0tJSclg9TqtP5lzuVyD85hAAIH0CnAR3vR6UhoCSQvoTzwOPPDApPOTEQGnBd577z2rCj3rJT7pB7Hp06fzU7l4FKazKsD+M6v8VD6OwEEHHST6Nzy98MIL0tTUZP0UafgyniOQaQE960X/7NTa2mqdBbtr1y7rDG17Po8IIJBeAQIw6fWkNASSFiD4kjQVGTMk0N3dbdWkpyQPT3oXJL3gaSQS4cJ8w3F4nnEB9p8ZJ6fCFAX0Iry33nqr9RMkPTuGhEAuCXz5y18W/emcps997nNy2mmn5VLzaAsCBSXAT5AKajjpDAIIIDB5gUAgYJ12rKcfD0/2vFAoNHwRzxFAAAEExhDQa2h99atflbfeess6uF20aNEYuVmEQOYFdJvUaxbply233Xab3HfffZlvBDUiUCQCnAFTJANNNzMvcO2111oX4YuvWS/Apz/lICGQiwL19fXWNWD0TJfy8vIhTdSzY/Q2lX6/f8h8niCAAAIIjC6gP+v44he/aF1w/7Of/aycddZZo2dmCQJZErjwwgutmj//+c/LxRdfLN/61rfksMMOkxkzZmSpRVSLQOEKEIAp3LGlZ1kWePnll0U/eMUnPQWZhECuCujtpjXpBSKHB2B0nl5AkoQAAgggkJzAjh075NJLLxW9psaVV1454qK8yZVCLgQyJ6Bfspx66qmydu1aefLJJ0VvUU1CAIH0Cvz/9s4E3qpx//9PFFI0IFMhmcqUWyrDrdyMIVHq0jXdKEPDpbgqP+G+QkhJV7xKXUPdKHOX6JLKWEIpFNGcIhUZQp7/9/P9/b7rv9baa5+z9zmn0977fJ7Xa5+11jM/72ftU8/3fAcKYMqWJ3sjgYDAxIkTg3vekEA+EDDnu4sXL4781Wvz5s1u+fLliU4l82FdnCMJkAAJlDeBpUuXqvAFvz/hNPqYY44p7ylwPBJIS2Djxo3uvvvuczA96tKlS6Re3bp19Rmh1JlIgATKngB9wJQ9U/ZIAiRAAnlJoHXr1qr5MmXKlMj8Z8yY4eDD4JRTTonk84EESIAESCCVALRd+/Tp43C9//77KXxJRcScbUxg1113dbNmzXJjx45169evj8xm6tSp+kxn5xEsfCCBMiNADZgyQ8mOSIAESCC/CcDsCE74xo0b54YPH67q8tCGGTZsmGvRogUFMPm9vZw9CZBAORGAE9M1a9a4Qw45xD377LMpo8LMo2fPnin5zCCB8iJQqVIl161bN/X1MmDAADU1ghYs/gADB7z4g0zTpk3LazochwQqFAEKYCrUdnOxJEACJFA0ATjiQ6hpmNDhU716ddeyZUvXvXv3ohuylARIgARIQAlMmzZNr4sWLXL4xFPVqlUpgIlD4XO5E2jXrp063n/ooYfcwIEDdfwqVaq4iy66yDFUerlvBwesQAQqeUkVaL1cKgmQAAmQQAYEEG4afl/q1avnKlemrD4DZKxCAiRAAiRAAnlHYMuWLW7VqlUOV/h/4b/5ebeFnHCeEaAAJs82jNMlARIgARIgARIgARIgARIgARIgARLIPwJ0wpt/e8YZkwAJkAAJkAAJkAAJkAAJkAAJkAAJ5BkBCmDybMM4XRIgARIgARIgARIgARIgARIgARIggfwjQAFM/u0ZZ0wCJEACJEACJEACJEACJEACJEACJJBnBCiAybMN43RJgARIgARIgARIgARIgARIgARIgATyjwAFMPm3Z5wxCZAACZAACZAACZAACZAACZAACZBAnhGgACbPNozTJQESIAESIAESIAESIAESIAESIAESyD8CFMDk355xxiRAAiRAAiRAAiRAAiRAAiRAAiRAAnlGgAKYPNswTpcESIAESIAESIAESIAESIAESIAESCD/CFAAk397xhmTAAmQAAnkEYHPPvssj2abG1Nt2rSpw2fTpk25MSHOggRIgARIgARIgATKgAAFMGUAkV2QAAmQAAmQQJzAli1bXL9+/dwf/vCHeBGfiyEwZ84ch89vv/1WTE0WkwAJkAAJkAAJkED+EKjkJeXPdDlTEiABEiABEsgPAuvWrXO7776722GHHdzmzZvzY9I5MstKlSrpTNavX+9q1qyZI7PiNEiABEiABEiABEigdASoAVM6fmxNAiRAAiRAAiRAAiRAAiRAAiRAAiRAAsUSqFxsDVYgARIgARIggW1A4Pfff3cffvihmqLsvffe7thjj3V77rln2pmsWrXKzZs3zy1btswddNBB7qijjlINlHiDr7/+2q1du9bttttubq+99ooXa/vvv//e1a1b19WoUUPLoc3y1VdfuX322cfVqlXLfffdd27WrFlaF+PgA00XS6tXr3ZffPGFPkLRdMGCBXrfsGFDt912//u3D+S/++67btGiRVrWqFEjh/Jq1apZN8VebV7gU7t2bbdixQr31ltvuZ9//tkdc8wx7sgjjyyyjw0bNihj+Kk58MADXePGjZVLvNE333zj1qxZ4/bdd1+38847u9dee81hf9q0aeN23HHHePXgubTzCzoK3WBtYLZw4UK3ceNG16BBA3fYYYc5MCgqzZ8/382ePdsdffTR+tl+++3dxx9/7KpUqeIOPvjgoGnSXlkhxl68eLHudbiNlYMJ5jZ37lw1nwJPzA1jMZEACZAACZAACZCAgwkSEwmQAAmQAAnkEoFrr73Wi+kJTGSDz0477eT79+/v5ZAbmeq3337rr7nmGi+H3KAu2uH5hhtu8HJojtS/5ZZbtF7v3r0j+fZw9tlna/mjjz5qWf6ee+7RvAcffNDfe++9XoQokbEOPfRQL4fuoP5VV10VKbd1iGBH64hgycvBPKXOLrvs4seOHRv0U9yNzeu+++5TBjaOXc8888yU9aNP8a3ib7vtNl+5cuXIHMDs5ptv9r/++mtk6Ntvv13rjRw50rdo0SJos8ceeyT2b41LOj+bv5ggWVd6HT16tN91112D8a0e9qNHjx7+xx9/jNTHw4QJE7wI0yJtRHjilyxZovsogqdIG+szPjYqiV8a7SfeBmUi4PEi3ImMg76OOOKIyLuBukwkQAIkQAIkQAIVkwA1YOR/R0wkQAIkQAK5Q2DAgAFu6NChqu0yePBg1U6YOnWqGz9+vBNBgGpciJBAJyz/dLuOHTuqRga0U3r16uVEGKKaDcOGDXN33XWXaqpAY8P8ipRmpSNGjFBtls6dO7tWrVq5lStXuscee0y1MURwo1ov0HY4//zzVYMGa8HzkCFDdFhoi0C7pm3btg4aO927d3ennXaa+oh5+eWXdY2XXXaZatmcc845GU8VXKChIkIl1759e9XWGTRokPvPf/7jRDjlRHAR6atTp07u6aefdnXq1HF9+vRRzRtowQwfPtyJYEbXOGnSpEgbPIjwyaGeCBXcTz/95Jo0aVKkBox1kO38rF34inkNHDhQtZrA7cQTT3TQZpo8ebKbMWOGw97sv//+rm/fvkGzt99+211yySWqrSOCN9e6dWv3+eefu1tvvdWdcMIJ+CNUULc0N9CsadmypWoedejQwZ133nmq6TRlyhT3yCOPuGbNmrk333xTeZVmHLYlARIgARIgARLIcwIVU+7EVZMACZAACeQigSeffFI1CMQ0yIsZT2SK48aN0zIx0fESnljLRGCgeWKG4kUAEakv5jheTGa0fMyYMUFZaTRg5J98P2rUqKAv3IhJiofmCsrkwB2UidmO5olpUpCHGxEYaL4IcCL5eLjuuuu0TIQvKWVJGaZhgrFF4BSpImZSXkyxtL+wNs9LL72keWAsZlWRNpjzAQccoOUiEArKTAMG40DbxpIIYew28VqS+aEjjIOPaaFgHGjbIA/vSDxBAwpl0ECx9MMPP3gxWUtsg3fLyuLaLPGxrT9ckzRgoJFlWkEi2AlX13tjcPzxx6eUMYMESIAESIAESKBiEaATXvmfFhMJkAAJkEBuEBDhgE5ETIdS/LNccMEFTkx7nJiaOPguQRKTIL1COwLaHOEEfyXQekAyDRR9KMUP+Enp2rVrpAfkwQcM0pdffhkpS3qw0MrQ3oCPlHDCfD/55BP3xBNPhLOLvYcfFGi6hJMIhTQMNvKef/75oOiOO+7Qe4wV96kDvzgXXnihlkPbJZ7gZ6Vnz55BtpiFBfdF3WQzv6R+RJiiWivQpIF2UTyde+65miXmaEER/AdBKwh7E28D3z9hTZmgUQluXn/9dffOO++ofxxoPMVTt27dHDjBNw/qMZEACZAACZAACVRcApUr7tK5chIgARIggVwjgEMzEsw54gkmRA888ECQDUEGzGGQYMaTlCwf9bZs2VJqZ6gwb0oyZdpvv/3UxOSXX35JmkYk76STTlLnvnAAC0eu4qfFnX766boGhK2G09Zs08knn+zEn0tKM5jpIH3wwQdBGcZFgmkUhAfxZM5sP/3003iRmnclrT+lYiwjm/nFmuojBEMQvoUTQlTD4e1HH32kZkgoM+EW7t9//31cnGin6DX+A8yvv/76eHbWz8ZTNIfczJkzE9vDZOu9995zYJpuPokNmUkCJEACJEACJFBQBFL/t1ZQy+NiSIAESIAE8oWAOH4NogVBoFFcQjQatKlevXpi5B60hxYMotxAMLJ06VKN9FNcv0WVm3AiXsciIIkSbbwo5VmcyLo33njDwccLIiU9/vjj+kF0pOOOO85dffXVgRZKSuM0GfB9kpTq1aun2RhHzLZUQIGIRkhXXHGFXtP9WL58ufqmCUc5grZPSVKm88NepksQoD311FPu4Ycf1mhXiEplKUn4ZMI8RLNKSpm8Y0nt4nkmBISPFwjXikrwP8NEAiRAAiRAAiRQcQlQAFNx954rJwESIIGcI4BDNhIEK8UlCFaQoPUAwUeSZgbKTCvC6hfX7+bNm9NWsRDSaStkWACNCGhvvPrqq+ooFw54EVYZh3h84Dz2/vvvz7C39NUkKpAWwgSmatWqGj7bat95552R0NmWX9Q1LIwpql6mZfH5pWuH/b344ovVSTHqQKgCsyKElG7atKmDUEt8rESaI1w2Urr9hAZQtimpLxO6QfgCR8xFJYQGZyIBEiABEiABEqi4BCiAqbh7z5WTAAmQQE4RgIAEmhLQ1hAHug5RjeIJ0W7gO6V58+ZaF20kzLT6+oBfj3hatmxZEOlGnLhqsR280wl5MH55JMzj1FNP1Q/Gg/8YRG2CXxuYWlnEn0zmgnUmJctv3LixmhzVqlVLowhBCwZjl5dAwOYRn6Pl2/zi5fb8zDPPqPClRo0a6s8mbqKGiE5I4hDXmqh5Fx6g+ZSUbOx4GfYFgsCk9yPp3TjkkEO0C/jckfDp8e74TAIkQAIkQAIkQAIBATrhDVDwhgRIgARIYFsTaNSokU4B4ZOT0o033qhhp+fOnasChYYNG2q1CRMmJFV3lo8wwOYwtmbNmloXDlrjCc5elyxZEs8u0bNpy4SFAugImieHH364M2e41nn9+vVV6wV+YNBm/vz5VlTsFRo0pukTrmyhpBEu2hK0b5BgzpOUELIaXBG+uaxSNvNLGnP69OmafdZZZyX6BzIfN2EGtmY4dkbo73hK944V9X4k7YnxhHDQzLvCY0HLBwJDmJdNmzYtXMR7EiABEiABEiCBCkaAApgKtuFcLgmQAAnkMoH+/fvr9CS8dOAPxuYr4YfVNAcOWeHUFQmRfJAQHWfevHl6bz/g9FRCAOtjr169LFsdyeLhueeec+YnBM/QeujcuXOiIAPl2SYzgYFQYOXKlUHz2rVrOzhuhbZLOGoPKsyePVsP8WiLQ3umCdozgwYNilSHE1qYMcH06NJLLw3KoFmDBDYQGoQTtEUkFLY6i4VT2bJK2cwvaUyYGCHByW1YyIK8F1980Q0ePBi3qg2lN/IDJklt27bVSFNwthsWhMH8C/yTEhwtI0lI6UgxhEBDhw6N5OFBwom71q1ba2Su7t27OwmZHakDoeGsWbNUoGZCoUgFPpAACZAACZAACVQYAjRBqjBbzYWSAAmQQO4TgJbAlVdeqWY40FpB6Gk4koUJCrReoFUCp7XmiwThh+ELZOLEiRpdBgIUmIRYKGc43x02bJjr0qVLsHgIb1AHh3CYsiBSEsxHpk6dqofoM844w1k47KBRCW4wR5hRrVq1Sk194PT1hRdecH/5y190fdDawNowHxzMEabYBCIQGEBwkmmCwAbCKDj3hTAAAo/x48erQAKCq/DBH+XGGH5LOnbsqKGaIbB55ZVX1FkvhD8IBV5WKZv5JY2Jfb377rsdzIYwt/bt2+s7AKEI/OjAhAkCOGgwbdy4UaNMoR8ITObMmeMeeughFbZhb9euXevGjRsXEciEx4QTZOwFtIcQwhoCFvQNvzzQwMEextOIESO0Hkyh4JemXbt26vwZdRcsWKDvLZwHmyAp3p7PJEACJEACJEACFYSAOI9jIgESIAESIIGcIjBmzBhfp04dL/8UBx8xT/IiJEmc58iRI734eAnqSlQiLyGYvRx6E+uvXr3aSxhiLwKdoA36Fw0UL5okmvfoo48GbUVbRPMuv/zyIC98I+Y6Wi7CnnC2/+9//+tFCBOMYfMXoYwXrRQv0XuCMqxVtE58eNxIZwkPNq+uXbt6MW3yEkVI+8O6RAPEP/LIIwmt/jdLhBBenNlGxhehj+/du7cXzZxIO9Ew0noSCjqSX9xDSedn+y6hpoMhJk+e7MVMKzJfvCOiLeVFe8m3adNGy8aOHRu0wQ1Yi5DNi28XLRdnzV6EXn7KlCn6LJGdIvXxgD5E0yoYSwR0vkePHl60b7z4HfJJbSQqk+/QoYPHu2fzx1X87HjR0kkZgxkkQAIkQAIkQAIVj0AlLFn+g8BEAiRAAiRAAjlHANoj0OaAOQxCSheXRLCiTlehuWAmQEW1QWhmaCg0aNBAndMWVbc0ZQiZDIfBMJ8KJ2hswOxHBA0O/m/gJDebNGTIENe3b18ngiE3atQoNaOCtgY0a+BLJpO0bt06jcCEseGHxnzlZNK2uDplMb/wGDATAy+8F9izdGHBw23s3vYa2k9YKzR+oBmE0NoIaZ6UkA//MUceeaT6HEqqE8+DiRRCU2/YsEF5JjmHjrfhMwmQAAmQAAmQQMUgQBOkirHPXCUJkAAJ5CUBmPAkRUNKtxgcyLM5lIvGSFa+VtKNW1x+ukN4tWrVVPBSXPtMyxHBJ9vIRhAKxUM4ZzpetvVKMr/wGGgPgQk+2aaS7DWEPNkm0WpSJ8bZtmN9EiABEiABEiCBwiewXeEvkSskARIgARIgARIgARIgARIgARIgARIggW1LgAKYbcufo5MACZAACZAACZAACZAACZAACZAACVQAAjRBqgCbzCWSAAmQAAkUJgGETO7UqZNr2rRpTi4wl+cHPzBgJ458c5IdJ0UCJEACJEACJFB4BOiEt/D2lCsiARIgARIgARIgARIgARIgARIgARLIMQI0QcqxDeF0SIAESIAESIAESIAESIAESIAESIAECo8ABTCFt6dcEQmQAAmQAAmQAAmQAAmQAAmQAAmQQI4RoAAmxzaE0yEBEig8AuPHj1cfHbfddlvhLa4Cr6h169a6r1999VUFplC2S4cvG3w2bdoUdJyLnBcuXKjzPO+884J5FuLNCSecoOtct25dIS6PayIBEiABEiCBcidAJ7zljpwDkgAJVDQCa9ascXPmzHFHHXVURVt6Qa/3gw8+cN9995375ZdfCnqd5bk4fE+Qfvvtt2DYXOT8ww8/6Hca+1/ICex/+uknvuOFvMlcGwmQAAmQQLkSoACmXHFzMBIggYpI4JhjjnE9e/Z0zZs3r4jL55pJoFQEunfv7n7++We3yy67lKofNiYBEiABEiABEiCBbU2AAphtvQMcnwRIoOAJwIQCHyYSIIHsCdx1113ZN2ILEiABEiABEiABEshBAhTA5OCmcEokQAKFRQD+E+AnpFatWm6fffYJFvfJJ5+47bbbzh166KHOe+/gV+Ldd991tWvXdk2aNInU3bx5s/v8889dpUqVXKNGjYI+wjfQEli8eLGrXLmy9hku27Bhg/vwww/dZ5995g488EDXuHFjt9tuu4Wr6P0333zjYDK17777up133tm99tpr7vfff3dt2rRxO+64Y1AfdWbPnu1Wrlzp9ttvP53T/vvvH5Qn3WQ6h6S24byPP/5Y13jIIYe4JUuWuJkzZzr4qsC6wgk8Fi1apFw3btzoGjRo4A477DC39957h6vpfTZ7kdI4lrFlyxb36aefai72Mmm8cBN7P/Bu4B2BWcusWbPcsmXL1GwNpms77LBDuEnkftWqVW7evHla/6CDDtI2u+++e6QOHorbWzDYfvvtHbjifQRn7DHeN7yPKLOEuU2fPt1VrVpVNbvq1atnRSnXbPYhpbFkgCWYYl5VqlRxq1evdt9++21S1Uhe3bp1XY0aNSJ5eJfxTsydO1fNnPA9wDsRXlukwf89LFiwQFkcffTRJTIlLMkef/31127t2rX6Pd1rr71SpoU9+P777114nTYOvr81a9Z0eO/x/YAZUatWrVydOnWCfvA75Z133tHfCQcffLD74x//qL+PggqxG+zje++9p9+nI444Qn+HhH8nxKrrY6bf+eLezaS+mUcCJEACJEACeUlA/pPFRAIkQAIksBUJ3HvvvV7+gfCXXXZZZJTq1at7OVj5FStWeDnQaB3Uw0cELb5///5efGFoGzkseRGYaJkcmiL92MODDz6o5WeeeaZlaXtx/utFKBPpXw6c/uabb/a//vprUBc3t99+u9YbOXKkb9GiRdBmjz328HIA07p9+vTxIhAIymzOp512mhdhQKQ/PGAN2cwhpYNYBsYWoY9/+umnPdZh4w8ZMiSoOXr0aL/rrrsGZVZHBF6+R48e/scffwzq4iabvbCG1v/SpUsty4ugwF944YU6rgh8fLgsqBS7ueeee7Q+9g/vCuZo88VVBHReBAaxVt6LEMJfc801EQaoDyY33HBDsF/WsLi9BQM5uHsR4nkR5ETmgHdPBIReDvL+rLPOipTttNNOHnNPStnug617/fr1QXdxzldffXVkfGsTvz722GNBH7iZP3++FwFKSlt895L4os3jjz/uRbAYaSMCKf/UU09pngguUK3YVJI9vuWWW3SM3r17J/Z/9tlna/mjjz4alNs4o0aN0u93+PsBPnj3kV566SV958PMmjVr5kXgE/SFGxGw6RhTpkxJ4SBCPv/yyy9H6ttDtt/54t5N65dXEiABEiABEsh3AvgrFxMJkAAJkMBWJFCUAAaHXhzwIFCAkAKCj/PPP18PPTgc4SBlqVevXpov/mQsK3I9/vjjtXzSpElBvkRp0Tz5y7cfPHiwf/755z0EFRgT/Xfo0CGoixs7COFgiXIcTiFI6NSpk9azAx7ycOh+8cUX/aBBg7xoaWh90SiI9IeHbOeQ0kEsAwIY0RRRgVS1atW8aL94CAFEY0Nr3nrrrToXHBDFf4jHQRx78Kc//SkQRN19992RXrEP2ewFGscFAxC+XHTRRTo2hCYQrGWSjClYQ/D25z//Wd+Dm266KdgnvB8mjEOfosmh68EeieaMv/POO/0zzzyjewFhGfLF7E3r2RyK21usX/yseNHY8dh/CAAhpMMz+jv88MN1TLxLEA7885//9KIZpWWiCeG//PJLG0qvJdkHjINPUQKYyZMn67wwN/tgPnfccYeuGe1F88WLpkswH9Eo0ncEZXjnx40b5//973/7Sy65JJi/aHcE9XHz9ttvq6ARQoy//e1vKnQRcygVmmK96CtbAUw2e1waAQzeP8wb39vhw4f7tm3bBoI9fCfwHWrfvr2+Z/369VNeWA/KwskEMKJ55OvXr699Qfj017/+VdcPYeELL7wQbqL32X7ni3s3UwZgBgmQAAmQAAnkKQEKYPJ04zhtEiCB/CFQlAAGhx4xrfASVSWyIGg2oAxaKJbEhEjzcMCOa66IeZKWQVMB2jJI+Cs3+oCWjZhAWTd6FZV/f8ABB2h5+K/YdhBCu/vuuy9oA80HJAkRrG2mTZumz/ZDzEI8hCFoJ5FTLLtEcwgap7kx7RvMX0wctJbND1cTQDz55JMpPRhXaEKEE4QPmHume4G2YQEMBCKXXnqp9gFBRZx3eKz4vQlgMH5Y4IZ60EaBUARl0EKwhEM18iAIE3Mwy9YrBD/QZEH5mDFjgrLi9tYYQDgn5i9Bu+XLl2tf6E/MWjz22hLeQ9MqCY9V0n3AGPgUJYCxsePX119/XQUL0PaaOnVqUIy9MW0uCIXiyfhDgGlJzMBUsIW5QFATTuBr71i2Ahj0l+kel0YAg3HiQsauXbsG+/j3v/89vCQ/ceJELYOgL5xMAIP3DL8zwgkCXYwDoRKEj5a2xu8d65tXEiABEiABEsh3AtvJP55MJEACJEAC25DAgAED1N9KeAry12l9FK2CIBv+JxBRCb4hXnnllSAfN2Iqoc9dunQJ/IWIRoDmyUHO7bnnnnpvP+D/RUxl9FEERJYdXOG3BJGbLImGid6KFoZezceJlcNHBULWwjcM/GpYKs0crI901xtvvDHw8WHzQ3hgOWQ7ETY40SRKaXruuedqXjofIpnuRbhj+BW54oor3L/+9S9duwgCUniH66e7hw8bOSRHipFn4cvD74JoH2k90ZqK+PVAJvx/YM+RRNtJr+Ef6fbW6ojmiwv7kIGPEdF+0OLLL7/chf2RwN+QCOW0DP54LJV2H6yfTK/wX4O9RUjwBx54wJ188slBU+wHfJ2AC/Y3nrp16+bw/rz11ltaD+UIhw3fOvjOiUZSpAn6ufbaayN5mT5ks8eZ9plUD3vct2/fSFHLli31GX6k4hxEQKVlIlyKhAC3DvA+4XdGOIlplPqpEtMu98YbbwRFpfnOF/duBoPwhgRIgARIgATylEDlPJ03p00CJEACBUMATkDjSf4SrVk4UIaT+JFRQQcELmJWEBSZAAblluBEFUlMERwOofGEww5SXJiCPDgGxkEtnnDIhTPfq666yo0dO1bncMYZZ+ghXLQB4tXVkSsyM50DHIPC8WhSwsEXTostJXHDIRFzCyfRplDHqx999JET8xUtMkFSuB7uk/pMtxfWVkzDnJhh6CMO/2HhhdXJ5JqOOcZ/8803VbiAfjB3OFNGEr87eo3/sHzUgwNb8LeUbhwrF20Huw2u2FsIgOAIN55MuAcnrZZKuw/WTyZXCP3wXcA+X3/99SoMC7ez74FoTKlD2nCZ3YsWhzqYxXcBwgg46UU68cQTrUrkeuqppzoIqrJN6djH9zjbfuP10+0h6uF7Hw/pbXsIYaJoNamT63Cfp5xySvhR7+GAt3nz5k5M3xwEYCbgMd6ZfufDHafjE67DexIgARIgARLIZwIUwOTz7nHuJEACBUHABCHhxYiZjT6KmmU420HDBX/Zfu6559ymTZucmI048VWhEZKgeWLaJ4g+gsgiSNDOKCqJiYmD4CMc0SQeUcja/8///I9GXoHWDCL14IO/jotJhhPfGg7lFumpJHOAFoL4arHhIldo/oSFG+nmCIGD+KlwDz/8sEYHQgQqS9DYKCplsxfWD4QviBgljn3dddddp9oAYYGH1SvumjQ22sTfBUS6wiEZex/XSrAxIKxCxCAI8MQRcCRCVDpu1haCingyYVw8qhDqmVDM6ljb0uyD9VHcFczFKbBGw4JwUHzhpDQxYRWEWCeddFJKeTgDkcaQTAAD7Z+klC4/qW44L9M9DrcpyX1J9xBjxfcR2kEmoInPxaJfGbeSfOcz+b0TH5fPJEACJEACJJCvBIr+n2i+rorzJgESIIE8ImAH2EymjLDG55xzjhOfDfqXZ3H66sTJrDYVHyRBF2HBDQ6ldogPKhRzEz4UhavicCa+JZw4JXUShciJ/xgn/mDULApmMc8++6wT/xsOGgUlmQMEFxBmJKX4wTBpjhjz4osvduPHj9cucFCGKRJMSWAqI35bnPj6SOpe87LZC+sEIbAx3nHHHacmLDD7kShEVpzxNdOxIVhBgiYM1hvnYmWm5WP1tZH8SOJmZbiaOVc4L7yX4fzwfbhOafch3G+6ewh4YB6E0MjYW2iBJTG0eUH4IpGD0nWn+TDxQ7J3EIKupBRnmlQnKS9pfkn1MsmD0DRdKukeoj/jZX2Dc7pkGnoIeY0UbluWv3fSjc98EiABEiABEsg3AhTA5NuOcb4kQAIVnoBEIFEBzIQJE9wFF1zgnnjiCRWwQDvGkkQJUm0RaMHAXMIOllZe2is0LOAjBh8cBCF0ufLKK93KlSudOBl14sDXlWQOMGOA/5CSJphDQBgCTQ2J+BSYRVh/EBohwdSirJKEAXYwIYH5EXz3DBw4UA/6DRs2LKshIv2Ik1zVboHJD8xvwj5ZrOKyZcuCwzC0k8o7lcc+wAcJtI+ghYG9NqFJfK1mNgWzm0x9tzRp0kS7gfZQUkqXn1S3pHmmRZVOCPTFF1+UtOus2mF8mHfh+xxPxkHClmtRSb7z8T75TAIkQAIkQAKFTOD/G9MX8iq5NhIgARIoIALwxwAByKuvvqpaMHAoCzOMsHkOlgstFCSY4ySl0aNHOwgJJBRvUnFKHkyVWrVqpX5ANm7cGJRDowLjm9NPM99AhbKeQzBompvp06drCeZjPinCVeEoGMm0Q8JlJb03syZoJkHbBoIRaCMVpTlQ0rHQDgdzE+5ACJeULL9Zs2aJGi1Jbcoyb2vvA7SMJAy2+jKBX590pj1Yk72DM2bMCMzywmuFGRN8mUCDCdpcSOZYWMKsO4noFK6u9xKRKiWvrDNMqwRCtniCkHJJyOlxvLysn5PWC19N2GdoYJnpI8Y13mX1e6es18L+SIAESIAESGBbEqAAZlvS59gkQAIkUAICOIDDzAaaJ/YX/bDzXesSmhhIEmbX4fAZTvjLNfyVwOlokr+IcF27hzkP/I/Ap0ZSdB0JP6tVwz5cynoONpd0V5gYIc2cOTNFyILDtITO1fKww1jNKKMf999/v4OZGHzjgPvWSvC7g4RoT/PmzdN7+wGTHBsbDoK3Rdqa+zBp0iR1tgszIBzyLUpUunVCaNi6dWsH/yTdu3dPEaggmhb2C9F8TPOlUaNGaqomIbddv379Am0ijIHvjPFNN2ZZ5MMhLRL8PcHxtSUI9jp37pzyflv51rjCpC4s8IFWDN4tCK8QTc3mirHL+zu/NdbLPkmABEiABEhgaxGgCdLWIst+SYAESGArEoDABeFeYfIDE5TTTz89ZTQcOmEWBN8s8H/RsWNHPay+//77GsYaTnzxl/9M/ZWY/xccuP7xj3+oGRQ0TXAIQ1hsOOKEeQyEQ5bKeg7Wb7orDqbwUQMTHKwNJkHQ0MFf6qExhL/UQ2ABDQJo8SQ5lU3Xdyb5cFYKB8XQgDFTJBzmyzrB4Sy0beALCFF7sG6Y2iAaDUzS4Jtj2LBh6rS5rMfOpL+ttQ8QOsLvEXyNgPXQoUPdoEGDgghR4blB+wcMkEaMGKHaWzBBgz+gdu3aqRkXTJgWLFigvmPgtNkERxBywp8R+oA5HYRaiCyF0NTjxo1TjZt0oczDcyjNPUJpY08XLVqk2lwYH2ZUMPeDMAnRx0zoWZpximsL/1HggfcM3ydwh8kXhELQxLOw09ZPeX/nbVxeSYAESIAESCAvCMh/YphIgARIgAS2IgE5kCOUkRehSWQUiWKj+StWrIjk40FC/mqZmCGklFmGhMjVOhJ617ISr3Jg9KK9onUxD3yqVq3qxYeGl0NkpI1oVGi5hHKO5IcfxMeKl7DEkf7E/4aXQ62XSEXhqsF9NnMIGqW5kQOhji2mGYk1xCTF169fPzK/OnXq+OHDh3vRHvBt2rTRMgmjHbQvyV7IYV37EW2ioB+7Eb87Wnbsscd6MXey7MSraFNo3csvvzyxXEzEtFyECSnlI0eO9OLjRcuxr2CD90KECSl1i9vbohjYekS4k9LvTTfdpOOLIC9SVpJ9sPdTfI4EfYU5i+AsWKvVTXcVU72gD9xINCwvkbqUUbiN+Efyoh0VqWsPElLZi9PmYExxoutFwyv4fuJ7kEkq6R6LBo4X4arHuDZnEej52bNnexE8aZ74IAqmUNQ4EmFM64v5WlDfbvCOWv8iULVs/T0hQh+P31F4l62OCGS8ODT2YoYU1I3fZPOdL+7djPfNZxIgARIgARLIVwKVMHH5B5WJBEiABEigwAmsW7fOLVy4UJ1pioCiVL5B8E8HtAGgaQJfFTBByCTCS1nOoajtgpkGzKwwxwYNGhTpI6SofvKlDKYyWC/McdI5o90Wa8nFfYD/H5jRQYsE34MkJ8ZxVmALbTNoM5lvlnidrfkMbTVo6uBdjvt62prjxvtGKHgRDqufl0zfs/L6zsfnymcSIAESIAESyEUCFMDk4q5wTiRAAiRAAiRAAiRAAiRAAiRAAiRAAgVFgE54C2o7uRgSIAESIAESIAESIAESIAESIAESIIFcJEABTC7uCudEAiRAAiRAAiRAAiRAAiRAAiRAAiRQUAQogCmo7eRiSIAESIAESIAESIAESIAESIAESIAEcpEABTC5uCucEwmQAAmQAAmQAAmQAAmQAAmQAAmQQEERoACmoLaTiyEBEiABEiABEiABEiABEiABEiABEshFAhTA5OKucE4kQAIkQAIkQAIkQAIkQAIkQAIkQAIFRYACmILaTi6GBEiABEiABEiABEiABEiABEiABEggFwlQAJOLu8I5kQAJkAAJkAAJkAAJkAAJkAAJkAAJFBQBCmAKaju5GBIgARIgARIgARIgARIgARIgARIggVwkQAFMLu4K50QCJEACJEACJEACJEACJEACJEACJFBQBCiAKajt5GJIgARIgARIgARIgARIgARIgARIgARykQAFMLm4K5wTCZAACZAACZAACZAACZAACZAACZBAQRGgAKagtpOLIQESIAESIAESIAESIAESIAESIAESyEUCFMDk4q5wTiRAAiRAAiRAAiRAAiRAAiRAAiRAAgVF4P8B6BWCI4f6rEgAAAAASUVORK5CYII=" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
ggpubr::gghistogram(AEDB, \Mast_cells_plaque\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\, 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \number of mast cells\,
                    xlab = \counts per plaque\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
ggpubr::gghistogram(AEDB, \MastCells_rankNorm\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\, 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \number of mast cells\,
                   xlab = \counts per plaque\ninverse-rank normalized number\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
ggpubr::gghistogram(AEDB, \vessel_density_averaged\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\, 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \number of intraplaque neovessels\,
                    xlab = \counts per 3-4 hotspots\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
ggpubr::gghistogram(AEDB, \VesselDensity_rankNorm\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\, 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \number of intraplaque neovessels\,
                   xlab = \counts per 3-4 hotspots\ninverse-rank normalized number\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

<img 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" />

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Here we calculate the _plaque instability/vulnerability_ index


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<!-- rnb-source-begin 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 -->

```r

# Plaque vulnerability

# SPSS code

# 
# *** syntax- Plaque vulnerability**.
# COMPUTE Macro_instab = -999.
# IF macrophages.bin=2 Macro_instab=1.
# IF macrophages.bin=1 Macro_instab=0.
# EXECUTE.
# 
# COMPUTE Fat10_instab = -999.
# IF Fat.bin_10=2 Fat10_instab=1.
# IF Fat.bin_10=1 Fat10_instab=0.
# EXECUTE.
# 
# COMPUTE coll_instab=-999.
# IF Collagen.bin=2 coll_instab=0.
# IF Collagen.bin=1 coll_instab=1.
# EXECUTE.
# 
# 
# COMPUTE SMC_instab=-999.
# IF SMC.bin=2 SMC_instab=0.
# IF SMC.bin=1 SMC_instab=1.
# EXECUTE.
# 
# COMPUTE IPH_instab=-999.
# IF IPH.bin=0 IPH_instab=0.
# IF IPH.bin=1 IPH_instab=1.
# EXECUTE.
# 
# COMPUTE Instability=Macro_instab + Fat10_instab +  coll_instab + SMC_instab + IPH_instab.
# EXECUTE.

require(labelled)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)

table(AEDB$Macrophages.bin)

      no/minor moderate/heavy 
          1602           1215 

table(AEDB$Fat.bin_10)

 <10%  >10% 
 1226  1628 

table(AEDB$Collagen.bin)

      no/minor moderate/heavy 
           540           2297 

table(AEDB$SMC.bin)

      no/minor moderate/heavy 
           870           1962 

table(AEDB$IPH.bin)

  no  yes 
1223 1628 

# Fix plaquephenotypes
attach(AEDB)
# mac instability
AEDB[,"MAC_Instability"] <- NA
AEDB$MAC_Instability[Macrophages.bin == -999] <- NA
AEDB$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
AEDB$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1

# fat instability
AEDB[,"FAT10_Instability"] <- NA
AEDB$FAT10_Instability[Fat.bin_10 == -999] <- NA
AEDB$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
AEDB$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1

# col instability 
AEDB[,"COL_Instability"] <- NA
AEDB$COL_Instability[Collagen.bin == -999] <- NA
AEDB$COL_Instability[Collagen.bin == "no/minor"] <- 1
AEDB$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0

# smc instability
AEDB[,"SMC_Instability"] <- NA
AEDB$SMC_Instability[SMC.bin == -999] <- NA
AEDB$SMC_Instability[SMC.bin == "no/minor"] <- 1
AEDB$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0

# iph instability
AEDB[,"IPH_Instability"] <- NA
AEDB$IPH_Instability[IPH.bin == -999] <- NA
AEDB$IPH_Instability[IPH.bin == "no"] <- 0
AEDB$IPH_Instability[IPH.bin == "yes"] <- 1

detach(AEDB)

table(AEDB$MAC_Instability, useNA = "ifany")

   0    1 <NA> 
1602 1215  974 

table(AEDB$FAT10_Instability, useNA = "ifany")

   0    1 <NA> 
1226 1628  937 

table(AEDB$COL_Instability, useNA = "ifany")

   0    1 <NA> 
2297  540  954 

table(AEDB$SMC_Instability, useNA = "ifany")

   0    1 <NA> 
1962  870  959 

table(AEDB$IPH_Instability, useNA = "ifany")

   0    1 <NA> 
1223 1628  940 

# creating vulnerability index
AEDB <- AEDB %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
                                )

table(AEDB$Plaque_Vulnerability_Index, useNA = "ifany")

   0    1    2    3    4    5 
1324  655  728  676  298  110 

# str(AEDB$Plaque_Vulnerability_Index)

Prepare baseline characteristics

We are interested in the following variables at baseline.

• Age (years)
• Female sex (N, %)
• Hypertension (N, %)
• SBP (mmHg)
• DBP (mmHg)
• Diabetes mellitus (N, %)
• Total cholesterol levels (mg/dL)
• LDL cholesterol levels (mg/dL)
• HDL cholesterol levels (mg/dL)
• Triglyceride levels (mg/dL)
• Use of statins (N, %)
• Use of antiplatelet drugs (N, %)
• BMI (kg/m²)
• Smoking status (N, %)
  • Never smokers
  • Ex-smokers
  • Current smokers
• History of CAD (N, %)
• History of PAD (N, %)
• Clinical manifestations
  • Asymptomatic
  • Amaurosis fugax
  • TIA
  • Stroke
• eGFR (mL/min/1.73 m²)
• stenosis
• year of surgery
• plaque characteristics
• PCSK9

Fix things for CAC

For this project we also fix the CAC levels for analyses.

Measurement: This was measured in citrate plasma, at pg/mL using a LUMINEX assay.

# Fix hormones
attach(AEDB)
AEDB[,"Plasma_PCSK9"] <- NA
AEDB$Plasma_PCSK9 <- as.numeric(AEDB$PCSK9_plasma)
AEDB$Plasma_PCSK9[PCSK9_plasma == -999] <- NA
AEDB$Plasma_PCSK9[PCSK9_plasma == -888] <- NA
AEDB$Plasma_PCSK9[PCSK9_plasma == -777] <- NA
AEDB$Plasma_PCSK9[PCSK9_plasma == -666] <- NA

detach(AEDB)

AEDB$Plasma_PCSK9_rankNorm <- qnorm((rank(AEDB$PCSK9_plasma, na.last = "keep") - 0.5) / sum(!is.na(AEDB$PCSK9_plasma)))

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "PCSK9_plasma", "Plasma_PCSK9", "Plasma_PCSK9_rankNorm"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

```r
library(labelled)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$PCSK9_plasma <- as.numeric(AEDB$PCSK9_plasma)

ggpubr::gghistogram(AEDB, \Plasma_PCSK9\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\,
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \PCSK9 (citrate-plasma)\,
                    xlab = \pg/mL\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
ggpubr::gghistogram(AEDB, \Plasma_PCSK9_rankNorm\, 
                    # y = \..count..\, 
                    color = \white\,
                    fill = \Gender\,
                    palette = c(\#1290D9\, \#DB003F\), 
                    add = \median\, 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = \black\, linetype = 2), 
                    title = \PCSK9 (citrate-plasma)\,
                    xlab = \pg/mL\ninverse-rank normalized\, 
                    ggtheme = theme_minimal())

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiVXNpbmcgYGJpbnMgPSAzMGAgYnkgZGVmYXVsdC4gUGljayBiZXR0ZXIgdmFsdWUgd2l0aCB0aGUgYXJndW1lbnQgYGJpbnNgLlxuIn0= -->

Using bins = 30 by default. Pick better value with the argument bins.

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

<img 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" 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```r
cat("====================================================================================================\n")

====================================================================================================

cat("SELECTION THE SHIZZLE\n")

SELECTION THE SHIZZLE

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")

- sanity checking PRIOR to selection

library(data.table)
require(labelled)
ae.gender <- to_factor(AEDB$Gender)
ae.hospital <- to_factor(AEDB$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))

        Hospital
Sex      St. Antonius, Nieuwegein UMC Utrecht
  female                      524         636
  male                       1211        1420

ae.artery <- to_factor(AEDB$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"))

                                                                                         Artery
Sex                                                                                       female male
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA      0    0
  carotid (left & right)                                                                     805 1781
  femoral/iliac (left, right or both sides)                                                  320  796
  other carotid arteries (common, external)                                                   17   35
  carotid bypass and injury (left, right or both sides)                                        6    3
  aneurysmata (carotid & femoral)                                                              1    0
  aorta                                                                                        3    5
  other arteries (renal, popliteal, vertebral)                                                 4    9
  femoral bypass, angioseal and injury (left, right or both sides)                             4    2

rm(ae.gender, ae.hospital, ae.artery)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)
AEDB$EP_major_time <- as.numeric(AEDB$EP_major_time)

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)

AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)

AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$EP_major <- to_factor(AEDB$EP_major)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)
AEDB$Plaque_Vulnerability_Index <- to_factor(AEDB$Plaque_Vulnerability_Index)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd" & 
                     !is.na(AsymptSympt2G))
# AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)

[1] 2421 1113

AEDB.full <- subset(AEDB,
                    informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd")
# AEDB.CEA[1:10, 1:10]
dim(AEDB.full)

[1] 3458 1113

cat("===========================================================================================\n")

===========================================================================================

cat("CREATE BASELINE TABLE\n")

CREATE BASELINE TABLE

# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   # "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   # "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
                   "MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
                   "Neutrophils_rankNorm", "MastCells_rankNorm",
                   "IPH.bin", "VesselDensity_rankNorm",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index",
                   "Plasma_PCSK9", "Plasma_PCSK9_rankNorm")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con

Athero-Express Biobank Study: baseline characteristics

Showing the baseline table of the whole Athero-Express Biobank.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.full, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]

                                      
                                       level                                                                     Overall               Missing
  n                                                                                                                   3458                    
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                       45.3 (1567)       0.0   
                                       UMC Utrecht                                                                    54.7 (1891)             
  ORyear % (freq)                      No data available/missing                                                       0.0 (   0)       0.0   
                                       2002                                                                            2.5 (  86)             
                                       2003                                                                            5.5 ( 191)             
                                       2004                                                                            7.7 ( 265)             
                                       2005                                                                            8.0 ( 276)             
                                       2006                                                                            7.3 ( 254)             
                                       2007                                                                            6.0 ( 206)             
                                       2008                                                                            5.5 ( 190)             
                                       2009                                                                            7.1 ( 246)             
                                       2010                                                                            8.0 ( 278)             
                                       2011                                                                            7.0 ( 243)             
                                       2012                                                                            8.2 ( 284)             
                                       2013                                                                            6.8 ( 235)             
                                       2014                                                                            8.1 ( 281)             
                                       2015                                                                            2.2 (  77)             
                                       2016                                                                            3.5 ( 121)             
                                       2017                                                                            2.3 (  79)             
                                       2018                                                                            2.2 (  77)             
                                       2019                                                                            2.0 (  69)             
  Age (mean (SD))                                                                                                   68.733 (9.214)      0.0   
  Gender % (freq)                      female                                                                         29.7 (1026)       0.0   
                                       male                                                                           70.3 (2432)             
  TC_final (mean (SD))                                                                                               4.769 (1.406)     45.9   
  LDL_final (mean (SD))                                                                                              2.762 (1.061)     53.6   
  HDL_final (mean (SD))                                                                                              1.207 (0.436)     50.2   
  TG_final (mean (SD))                                                                                               1.738 (1.099)     51.0   
  systolic (mean (SD))                                                                                             150.820 (24.992)    13.0   
  diastoli (mean (SD))                                                                                              80.072 (22.444)    13.0   
  GFR_MDRD (mean (SD))                                                                                              74.865 (24.412)     6.6   
  BMI (mean (SD))                                                                                                   26.395 (3.997)      5.8   
  KDOQI % (freq)                       No data available/missing                                                       0.0 (   0)       6.6   
                                       Normal kidney function                                                         21.7 ( 750)             
                                       CKD 2 (Mild)                                                                   47.9 (1656)             
                                       CKD 3 (Moderate)                                                               21.7 ( 750)             
                                       CKD 4 (Severe)                                                                  1.5 (  51)             
                                       CKD 5 (Failure)                                                                 0.6 (  22)             
                                       <NA>                                                                            6.6 ( 229)             
  BMI_WHO % (freq)                     No data available/missing                                                       0.0 (   0)       5.8   
                                       Underweight                                                                     1.1 (  37)             
                                       Normal                                                                         35.3 (1222)             
                                       Overweight                                                                     43.6 (1508)             
                                       Obese                                                                          14.1 ( 489)             
                                       <NA>                                                                            5.8 ( 202)             
  SmokerStatus % (freq)                Current smoker                                                                 34.3 (1187)       5.7   
                                       Ex-smoker                                                                      49.6 (1716)             
                                       Never smoked                                                                   10.4 ( 358)             
                                       <NA>                                                                            5.7 ( 197)             
  AlcoholUse % (freq)                  No                                                                             32.2 (1114)       4.1   
                                       Yes                                                                            63.7 (2203)             
                                       <NA>                                                                            4.1 ( 141)             
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                   73.3 (2534)       1.2   
                                       Diabetes                                                                       25.5 ( 883)             
                                       <NA>                                                                            1.2 (  41)             
  Hypertension.selfreport % (freq)     No data available/missing                                                       0.0 (   0)       3.6   
                                       no                                                                             23.7 ( 820)             
                                       yes                                                                            72.7 (2513)             
                                       <NA>                                                                            3.6 ( 125)             
  Hypertension.selfreportdrug % (freq) No data available/missing                                                       0.0 (   0)       5.0   
                                       no                                                                             29.0 (1002)             
                                       yes                                                                            66.0 (2284)             
                                       <NA>                                                                            5.0 ( 172)             
  Hypertension.composite % (freq)      No data available/missing                                                       0.0 (   0)       1.3   
                                       no                                                                             13.5 ( 466)             
                                       yes                                                                            85.2 (2947)             
                                       <NA>                                                                            1.3 (  45)             
  Hypertension.drugs % (freq)          No data available/missing                                                       0.0 (   0)       1.5   
                                       no                                                                             21.0 ( 725)             
                                       yes                                                                            77.5 (2681)             
                                       <NA>                                                                            1.5 (  52)             
  Med.anticoagulants % (freq)          No data available/missing                                                       0.0 (   0)       1.6   
                                       no                                                                             85.8 (2967)             
                                       yes                                                                            12.6 ( 434)             
                                       <NA>                                                                            1.6 (  57)             
  Med.all.antiplatelet % (freq)        No data available/missing                                                       0.0 (   0)       1.6   
                                       no                                                                             13.3 ( 459)             
                                       yes                                                                            85.1 (2943)             
                                       <NA>                                                                            1.6 (  56)             
  Med.Statin.LLD % (freq)              No data available/missing                                                       0.0 (   0)       1.5   
                                       no                                                                             21.0 ( 727)             
                                       yes                                                                            77.4 (2678)             
                                       <NA>                                                                            1.5 (  53)             
  Stroke_Dx % (freq)                   Missing                                                                         0.0 (   0)       7.5   
                                       No stroke diagnosed                                                            75.6 (2613)             
                                       Stroke diagnosed                                                               16.9 ( 586)             
                                       <NA>                                                                            7.5 ( 259)             
  sympt % (freq)                       missing                                                                         0.0 (   0)      28.3   
                                       Asymptomatic                                                                    9.1 ( 314)             
                                       TIA                                                                            28.0 ( 968)             
                                       minor stroke                                                                   11.8 ( 408)             
                                       Major stroke                                                                    6.9 ( 238)             
                                       Amaurosis fugax                                                                11.0 ( 380)             
                                       Four vessel disease                                                             1.1 (  39)             
                                       Vertebrobasilary TIA                                                            0.1 (   5)             
                                       Retinal infarction                                                              1.0 (  34)             
                                       Symptomatic, but aspecific symtoms                                              1.6 (  57)             
                                       Contralateral symptomatic occlusion                                             0.3 (  11)             
                                       retinal infarction                                                              0.2 (   6)             
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass       0.0 (   1)             
                                       retinal infarction + TIAs                                                       0.0 (   0)             
                                       Ocular ischemic syndrome                                                        0.5 (  16)             
                                       ischemisch glaucoom                                                             0.0 (   0)             
                                       subclavian steal syndrome                                                       0.1 (   2)             
                                       TGA                                                                             0.0 (   0)             
                                       <NA>                                                                           28.3 ( 979)             
  Symptoms.5G % (freq)                 Asymptomatic                                                                    9.1 ( 314)      28.3   
                                       Ocular                                                                         11.5 ( 396)             
                                       Other                                                                           3.2 ( 110)             
                                       Retinal infarction                                                              1.2 (  40)             
                                       Stroke                                                                         18.7 ( 646)             
                                       TIA                                                                            28.1 ( 973)             
                                       <NA>                                                                           28.3 ( 979)             
  AsymptSympt % (freq)                 Asymptomatic                                                                    9.1 ( 314)      28.3   
                                       Ocular and others                                                              15.8 ( 546)             
                                       Symptomatic                                                                    46.8 (1619)             
                                       <NA>                                                                           28.3 ( 979)             
  AsymptSympt2G % (freq)               Asymptomatic                                                                    9.1 ( 314)      28.3   
                                       Symptomatic                                                                    62.6 (2165)             
                                       <NA>                                                                           28.3 ( 979)             
  restenos % (freq)                    missing                                                                         0.0 (   0)       3.9   
                                       de novo                                                                        87.0 (3007)             
                                       restenosis                                                                      9.0 ( 312)             
                                       stenose bij angioseal na PTCA                                                   0.1 (   5)             
                                       <NA>                                                                            3.9 ( 134)             
  stenose % (freq)                     missing                                                                         0.0 (   0)       6.7   
                                       0-49%                                                                           0.7 (  23)             
                                       50-70%                                                                          6.9 ( 240)             
                                       70-90%                                                                         36.0 (1246)             
                                       90-99%                                                                         30.0 (1038)             
                                       100% (Occlusion)                                                               14.4 ( 497)             
                                       NA                                                                              0.1 (   3)             
                                       50-99%                                                                          2.5 (  86)             
                                       70-99%                                                                          2.7 (  93)             
                                       99                                                                              0.1 (   2)             
                                       <NA>                                                                            6.7 ( 230)             
  CAD_history % (freq)                 Missing                                                                         0.0 (   0)       2.0   
                                       No history CAD                                                                 64.1 (2216)             
                                       History CAD                                                                    33.9 (1173)             
                                       <NA>                                                                            2.0 (  69)             
  PAOD % (freq)                        missing/no data                                                                 0.0 (   0)       1.5   
                                       no                                                                             55.7 (1927)             
                                       yes                                                                            42.8 (1479)             
                                       <NA>                                                                            1.5 (  52)             
  Peripheral.interv % (freq)           no                                                                             68.0 (2352)       2.9   
                                       yes                                                                            29.1 (1006)             
                                       <NA>                                                                            2.9 ( 100)             
  EP_composite % (freq)                No data available.                                                              0.0 (   0)       5.1   
                                       No composite endpoints                                                         63.1 (2181)             
                                       Composite endpoints                                                            31.9 (1102)             
                                       <NA>                                                                            5.1 ( 175)             
  EP_composite_time (mean (SD))                                                                                      2.335 (1.166)      5.2   
  EP_major % (freq)                    No data available.                                                              0.0 (   0)       5.1   
                                       No major events (endpoints)                                                    83.3 (2882)             
                                       Major events (endpoints)                                                       11.6 ( 401)             
                                       <NA>                                                                            5.1 ( 175)             
  EP_major_time (mean (SD))                                                                                          2.735 (0.988)      5.2   
  MAC_rankNorm (mean (SD))                                                                                           0.004 (0.993)     32.9   
  SMC_rankNorm (mean (SD))                                                                                          -0.003 (1.004)     32.9   
  Macrophages.bin % (freq)             no/minor                                                                       41.6 (1437)      26.3   
                                       moderate/heavy                                                                 32.2 (1112)             
                                       <NA>                                                                           26.3 ( 909)             
  SMC.bin % (freq)                     no/minor                                                                       23.1 ( 799)      25.9   
                                       moderate/heavy                                                                 51.0 (1763)             
                                       <NA>                                                                           25.9 ( 896)             
  Neutrophils_rankNorm (mean (SD))                                                                                   0.010 (0.953)     91.0   
  MastCells_rankNorm (mean (SD))                                                                                    -0.009 (1.000)     93.0   
  IPH.bin % (freq)                     no                                                                             31.8 (1099)      25.3   
                                       yes                                                                            42.9 (1483)             
                                       <NA>                                                                           25.3 ( 876)             
  VesselDensity_rankNorm (mean (SD))                                                                                 0.017 (0.978)     48.3   
  Calc.bin % (freq)                    no/minor                                                                       38.0 (1314)      25.3   
                                       moderate/heavy                                                                 36.7 (1269)             
                                       <NA>                                                                           25.3 ( 875)             
  Collagen.bin % (freq)                no/minor                                                                       14.3 ( 496)      25.8   
                                       moderate/heavy                                                                 59.9 (2071)             
                                       <NA>                                                                           25.8 ( 891)             
  Fat.bin_10 % (freq)                   <10%                                                                          31.7 (1096)      25.3   
                                        >10%                                                                          43.0 (1488)             
                                       <NA>                                                                           25.3 ( 874)             
  Fat.bin_40 % (freq)                  <40%                                                                           59.5 (2056)      25.3   
                                       >40%                                                                           15.3 ( 528)             
                                       <NA>                                                                           25.3 ( 874)             
  OverallPlaquePhenotype % (freq)      atheromatous                                                                   14.7 ( 507)      25.9   
                                       fibroatheromatous                                                              22.2 ( 769)             
                                       fibrous                                                                        37.2 (1285)             
                                       <NA>                                                                           25.9 ( 897)             
  Plaque_Vulnerability_Index % (freq)  0                                                                              35.0 (1212)       0.0   
                                       1                                                                              17.1 ( 590)             
                                       2                                                                              19.0 ( 657)             
                                       3                                                                              18.0 ( 622)             
                                       4                                                                               8.0 ( 277)             
                                       5                                                                               2.9 ( 100)             
  Plasma_PCSK9 (mean (SD))                                                                                       31932.936 (18395.193) 59.2   
  Plasma_PCSK9_rankNorm (mean (SD))                                                                                 -0.002 (1.004)     59.2   

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]

                                      
                                       level                                                                     Overall               Missing
  n                                                                                                                   2421                    
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                       39.2 ( 948)       0.0   
                                       UMC Utrecht                                                                    60.8 (1473)             
  ORyear % (freq)                      No data available/missing                                                       0.0 (   0)       0.0   
                                       2002                                                                            3.3 (  81)             
                                       2003                                                                            6.5 ( 157)             
                                       2004                                                                            7.8 ( 190)             
                                       2005                                                                            7.6 ( 185)             
                                       2006                                                                            7.6 ( 183)             
                                       2007                                                                            6.3 ( 152)             
                                       2008                                                                            5.7 ( 138)             
                                       2009                                                                            7.5 ( 181)             
                                       2010                                                                            6.6 ( 159)             
                                       2011                                                                            6.7 ( 163)             
                                       2012                                                                            7.3 ( 176)             
                                       2013                                                                            6.2 ( 149)             
                                       2014                                                                            6.7 ( 163)             
                                       2015                                                                            3.1 (  76)             
                                       2016                                                                            3.5 (  85)             
                                       2017                                                                            2.7 (  65)             
                                       2018                                                                            2.7 (  66)             
                                       2019                                                                            2.1 (  52)             
  Age (mean (SD))                                                                                                   69.105 (9.302)      0.0   
  Gender % (freq)                      female                                                                         30.5 ( 738)       0.0   
                                       male                                                                           69.5 (1683)             
  TC_final (mean (SD))                                                                                               4.786 (1.457)     38.0   
  LDL_final (mean (SD))                                                                                              2.808 (1.081)     45.6   
  HDL_final (mean (SD))                                                                                              1.203 (0.440)     41.7   
  TG_final (mean (SD))                                                                                               1.709 (1.031)     42.8   
  systolic (mean (SD))                                                                                             152.419 (25.166)    11.3   
  diastoli (mean (SD))                                                                                              81.318 (25.188)    11.3   
  GFR_MDRD (mean (SD))                                                                                              73.121 (21.152)     5.4   
  BMI (mean (SD))                                                                                                   26.488 (3.977)      5.9   
  KDOQI % (freq)                       No data available/missing                                                       0.0 (   0)       5.5   
                                       Normal kidney function                                                         19.1 ( 462)             
                                       CKD 2 (Mild)                                                                   50.9 (1232)             
                                       CKD 3 (Moderate)                                                               22.8 ( 553)             
                                       CKD 4 (Severe)                                                                  1.3 (  32)             
                                       CKD 5 (Failure)                                                                 0.4 (  10)             
                                       <NA>                                                                            5.5 ( 132)             
  BMI_WHO % (freq)                     No data available/missing                                                       0.0 (   0)       5.9   
                                       Underweight                                                                     1.0 (  24)             
                                       Normal                                                                         35.1 ( 850)             
                                       Overweight                                                                     43.4 (1051)             
                                       Obese                                                                          14.5 ( 352)             
                                       <NA>                                                                            5.9 ( 144)             
  SmokerStatus % (freq)                Current smoker                                                                 33.2 ( 803)       5.9   
                                       Ex-smoker                                                                      48.0 (1163)             
                                       Never smoked                                                                   12.9 ( 313)             
                                       <NA>                                                                            5.9 ( 142)             
  AlcoholUse % (freq)                  No                                                                             34.5 ( 835)       4.0   
                                       Yes                                                                            61.5 (1488)             
                                       <NA>                                                                            4.0 (  98)             
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                   75.2 (1820)       1.1   
                                       Diabetes                                                                       23.7 ( 574)             
                                       <NA>                                                                            1.1 (  27)             
  Hypertension.selfreport % (freq)     No data available/missing                                                       0.0 (   0)       3.2   
                                       no                                                                             24.3 ( 589)             
                                       yes                                                                            72.4 (1754)             
                                       <NA>                                                                            3.2 (  78)             
  Hypertension.selfreportdrug % (freq) No data available/missing                                                       0.0 (   0)       4.4   
                                       no                                                                             29.9 ( 725)             
                                       yes                                                                            65.6 (1589)             
                                       <NA>                                                                            4.4 ( 107)             
  Hypertension.composite % (freq)      No data available/missing                                                       0.0 (   0)       1.2   
                                       no                                                                             14.6 ( 353)             
                                       yes                                                                            84.3 (2040)             
                                       <NA>                                                                            1.2 (  28)             
  Hypertension.drugs % (freq)          No data available/missing                                                       0.0 (   0)       1.4   
                                       no                                                                             23.3 ( 565)             
                                       yes                                                                            75.3 (1823)             
                                       <NA>                                                                            1.4 (  33)             
  Med.anticoagulants % (freq)          No data available/missing                                                       0.0 (   0)       1.6   
                                       no                                                                             87.3 (2114)             
                                       yes                                                                            11.1 ( 269)             
                                       <NA>                                                                            1.6 (  38)             
  Med.all.antiplatelet % (freq)        No data available/missing                                                       0.0 (   0)       1.5   
                                       no                                                                             12.2 ( 295)             
                                       yes                                                                            86.3 (2090)             
                                       <NA>                                                                            1.5 (  36)             
  Med.Statin.LLD % (freq)              No data available/missing                                                       0.0 (   0)       1.4   
                                       no                                                                             20.3 ( 491)             
                                       yes                                                                            78.3 (1896)             
                                       <NA>                                                                            1.4 (  34)             
  Stroke_Dx % (freq)                   Missing                                                                         0.0 (   0)       6.9   
                                       No stroke diagnosed                                                            71.5 (1731)             
                                       Stroke diagnosed                                                               21.6 ( 524)             
                                       <NA>                                                                            6.9 ( 166)             
  sympt % (freq)                       missing                                                                         0.0 (   0)       0.0   
                                       Asymptomatic                                                                   11.2 ( 270)             
                                       TIA                                                                            39.7 ( 961)             
                                       minor stroke                                                                   16.8 ( 407)             
                                       Major stroke                                                                    9.8 ( 238)             
                                       Amaurosis fugax                                                                15.7 ( 379)             
                                       Four vessel disease                                                             1.6 (  38)             
                                       Vertebrobasilary TIA                                                            0.2 (   5)             
                                       Retinal infarction                                                              1.4 (  34)             
                                       Symptomatic, but aspecific symtoms                                              2.2 (  53)             
                                       Contralateral symptomatic occlusion                                             0.5 (  11)             
                                       retinal infarction                                                              0.2 (   6)             
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass       0.0 (   1)             
                                       retinal infarction + TIAs                                                       0.0 (   0)             
                                       Ocular ischemic syndrome                                                        0.7 (  16)             
                                       ischemisch glaucoom                                                             0.0 (   0)             
                                       subclavian steal syndrome                                                       0.1 (   2)             
                                       TGA                                                                             0.0 (   0)             
  Symptoms.5G % (freq)                 Asymptomatic                                                                   11.2 ( 270)       0.0   
                                       Ocular                                                                         16.3 ( 395)             
                                       Other                                                                           4.3 ( 105)             
                                       Retinal infarction                                                              1.7 (  40)             
                                       Stroke                                                                         26.6 ( 645)             
                                       TIA                                                                            39.9 ( 966)             
  AsymptSympt % (freq)                 Asymptomatic                                                                   11.2 ( 270)       0.0   
                                       Ocular and others                                                              22.3 ( 540)             
                                       Symptomatic                                                                    66.5 (1611)             
  AsymptSympt2G % (freq)               Asymptomatic                                                                   11.2 ( 270)       0.0   
                                       Symptomatic                                                                    88.8 (2151)             
  restenos % (freq)                    missing                                                                         0.0 (   0)       1.4   
                                       de novo                                                                        93.7 (2268)             
                                       restenosis                                                                      4.9 ( 118)             
                                       stenose bij angioseal na PTCA                                                   0.0 (   0)             
                                       <NA>                                                                            1.4 (  35)             
  stenose % (freq)                     missing                                                                         0.0 (   0)       2.0   
                                       0-49%                                                                           0.5 (  13)             
                                       50-70%                                                                          7.8 ( 189)             
                                       70-90%                                                                         46.6 (1127)             
                                       90-99%                                                                         38.3 ( 927)             
                                       100% (Occlusion)                                                                1.3 (  31)             
                                       NA                                                                              0.0 (   1)             
                                       50-99%                                                                          0.6 (  15)             
                                       70-99%                                                                          2.8 (  68)             
                                       99                                                                              0.1 (   2)             
                                       <NA>                                                                            2.0 (  48)             
  CAD_history % (freq)                 Missing                                                                         0.0 (   0)       1.9   
                                       No history CAD                                                                 66.8 (1618)             
                                       History CAD                                                                    31.2 ( 756)             
                                       <NA>                                                                            1.9 (  47)             
  PAOD % (freq)                        missing/no data                                                                 0.0 (   0)       2.0   
                                       no                                                                             77.5 (1876)             
                                       yes                                                                            20.5 ( 497)             
                                       <NA>                                                                            2.0 (  48)             
  Peripheral.interv % (freq)           no                                                                             77.2 (1868)       2.9   
                                       yes                                                                            19.9 ( 482)             
                                       <NA>                                                                            2.9 (  71)             
  EP_composite % (freq)                No data available.                                                              0.0 (   0)       5.0   
                                       No composite endpoints                                                         70.6 (1709)             
                                       Composite endpoints                                                            24.4 ( 590)             
                                       <NA>                                                                            5.0 ( 122)             
  EP_composite_time (mean (SD))                                                                                      2.479 (1.109)      5.2   
  EP_major % (freq)                    No data available.                                                              0.0 (   0)       5.0   
                                       No major events (endpoints)                                                    83.3 (2016)             
                                       Major events (endpoints)                                                       11.7 ( 283)             
                                       <NA>                                                                            5.0 ( 122)             
  EP_major_time (mean (SD))                                                                                          2.707 (0.977)      5.2   
  MAC_rankNorm (mean (SD))                                                                                           0.177 (0.952)     29.7   
  SMC_rankNorm (mean (SD))                                                                                          -0.060 (0.962)     29.9   
  Macrophages.bin % (freq)             no/minor                                                                       34.9 ( 846)      24.1   
                                       moderate/heavy                                                                 40.9 ( 991)             
                                       <NA>                                                                           24.1 ( 584)             
  SMC.bin % (freq)                     no/minor                                                                       24.9 ( 602)      23.8   
                                       moderate/heavy                                                                 51.3 (1242)             
                                       <NA>                                                                           23.8 ( 577)             
  Neutrophils_rankNorm (mean (SD))                                                                                   0.030 (0.951)     87.4   
  MastCells_rankNorm (mean (SD))                                                                                    -0.010 (1.002)     90.0   
  IPH.bin % (freq)                     no                                                                             30.7 ( 744)      23.5   
                                       yes                                                                            45.8 (1108)             
                                       <NA>                                                                           23.5 ( 569)             
  VesselDensity_rankNorm (mean (SD))                                                                                 0.056 (0.981)     35.1   
  Calc.bin % (freq)                    no/minor                                                                       41.6 (1006)      23.4   
                                       moderate/heavy                                                                 35.1 ( 849)             
                                       <NA>                                                                           23.4 ( 566)             
  Collagen.bin % (freq)                no/minor                                                                       15.8 ( 382)      23.6   
                                       moderate/heavy                                                                 60.6 (1467)             
                                       <NA>                                                                           23.6 ( 572)             
  Fat.bin_10 % (freq)                   <10%                                                                          22.4 ( 542)      23.3   
                                        >10%                                                                          54.3 (1314)             
                                       <NA>                                                                           23.3 ( 565)             
  Fat.bin_40 % (freq)                  <40%                                                                           56.2 (1360)      23.3   
                                       >40%                                                                           20.5 ( 496)             
                                       <NA>                                                                           23.3 ( 565)             
  OverallPlaquePhenotype % (freq)      atheromatous                                                                   19.8 ( 480)      23.7   
                                       fibroatheromatous                                                              27.8 ( 672)             
                                       fibrous                                                                        28.7 ( 695)             
                                       <NA>                                                                           23.7 ( 574)             
  Plaque_Vulnerability_Index % (freq)  0                                                                              29.5 ( 713)       0.0   
                                       1                                                                              14.3 ( 347)             
                                       2                                                                              19.7 ( 478)             
                                       3                                                                              22.1 ( 535)             
                                       4                                                                              10.4 ( 251)             
                                       5                                                                               4.0 (  97)             
  Plasma_PCSK9 (mean (SD))                                                                                       31967.258 (18888.894) 54.8   
  Plasma_PCSK9_rankNorm (mean (SD))                                                                                 -0.007 (1.016)     54.8   

Athero-Express Genomics Study: prepare

Let’s combine the full Athero-Express Biobank Study with the key-table containing the AEGS data.

NOTE: this should sum to 2,124 samples with genotypes.

AEGS <- merge(AEDB.full, AEGS123.sampleList.keytable, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
                  all = TRUE)

dim(AEGS)

[1] 3571 1137

AEGS$UPID.y <- NULL
names(AEGS)[names(AEGS) == "UPID.x"] <- "UPID"
AEGS$Age.y <- NULL
names(AEGS)[names(AEGS) == "Age.x"] <- "Age"

table(AEGS$CHIP, useNA = "ifany")

AffyAxiomCEU     AffySNP5       IllGSA         <NA> 
         918          687          519         1447 

AEGS$GWAS <- AEGS$CHIP
AEGS$GWAS[is.na(AEGS$GWAS)] <- "not genotyped"
AEGS$GWAS[AEGS$GWAS != "not genotyped"] <- "genotyped"

table(AEGS$CHIP, AEGS$GWAS, useNA = "ifany")

              
               genotyped not genotyped
  AffyAxiomCEU       918             0
  AffySNP5           687             0
  IllGSA             519             0
  <NA>                 0          1447

Also a visualisation of the AEGS with AEDB overlaps.

library(UpSetR)
require(ggplot2)
require(plyr)

Loading required package: plyr
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Attaching package: ‘plyr’

The following object is masked from ‘package:ggpubr’:

    mutate

The following object is masked from ‘package:purrr’:

    compact

The following objects are masked from ‘package:dplyr’:

    arrange, count, desc, failwith, id, mutate, rename, summarise, summarize

require(gridExtra)

Loading required package: gridExtra

Attaching package: ‘gridExtra’

The following object is masked from ‘package:dplyr’:

    combine

require(grid)

AEDB.availGWAS = list(
AEGS1 = subset(AEGS, CHIP == "AffySNP5", select = c("STUDY_NUMBER"))[,1],
AEGS2 = subset(AEGS, CHIP == "AffyAxiomCEU", select = c("STUDY_NUMBER"))[,1],
AEGS3 = subset(AEGS, CHIP == "IllGSA", select = c("STUDY_NUMBER"))[,1],
AEDB = AEGS$STUDY_NUMBER)

p1 <- UpSetR::upset(fromList(AEDB.availGWAS), 
                    sets = c("AEDB", "AEGS1", "AEGS2", "AEGS3"), 
                    main.bar.color = c(uithof_color[15], uithof_color[2], uithof_color[3], uithof_color[21]), 
                    mainbar.y.label = "intersection sample size", 
                    sets.bar.color = c(uithof_color[15], uithof_color[2], uithof_color[3], uithof_color[21]), 
                    sets.x.label = "sample size", keep.order = TRUE)
pdf(paste0(PLOT_loc, "/", Today, ".overlap.AEDB_AEGS123.UpSetR.pdf"))
  p1
dev.off()

quartz_off_screen 
                2 

png(paste0(PLOT_loc, "/", Today, ".overlap.AEDB_AEGS123.UpSetR.png"))
  p1

dev.off()

quartz_off_screen 
                2 

p1

rm(p1)

table(AEGS$Artery_summary, AEGS$QC2018_FILTER)

                                                                                         
                                                                                          family_discard family_keep issue passed
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA              0           0     0      0
  carotid (left & right)                                                                              20          21    40   1819
  femoral/iliac (left, right or both sides)                                                            1           0     0     99
  other carotid arteries (common, external)                                                            0           0     0      9
  carotid bypass and injury (left, right or both sides)                                                0           0     0      1
  aneurysmata (carotid & femoral)                                                                      0           0     0      0
  aorta                                                                                                0           0     0      0
  other arteries (renal, popliteal, vertebral)                                                         0           0     0      1
  femoral bypass, angioseal and injury (left, right or both sides)                                     0           0     0      0

table(AEGS$informedconsent, AEGS$QC2018_FILTER)

                                                                                                 
                                                                                                  family_discard family_keep issue passed
  missing                                                                                                      0           0     0      0
  no, died                                                                                                     0           0     0      0
  yes                                                                                                         17          16    24   1411
  yes, health treatment when possible                                                                          2           2    10    312
  yes, no health treatment                                                                                     2           2     2     91
  yes, no health treatment, no commercial business                                                             0           0     0     15
  yes, no tissue, no commerical business                                                                       0           0     0      0
  yes, no tissue, no questionnaires, no medical info, no commercial business                                   0           0     0      0
  yes, no questionnaires, no health treatment, no commercial business                                          0           0     0      1
  yes, no questionnaires, health treatment when possible                                                       0           0     0      2
  yes, no tissue, no questionnaires, no health treatment, no commerical business                               0           0     0      0
  yes, no health treatment, no medical info, no commercial business                                            0           0     3     10
  yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business              0           0     0      0
  yes, no questionnaires, no health treatment                                                                  0           0     0      0
  yes, no tissue, no health treatment                                                                          0           0     0      0
  yes, no tissue, no questionnaires                                                                            0           0     0      0
  yes, no tissue, health treatment when possible                                                               0           0     0      0
  yes, no tissue                                                                                               0           0     0      0
  yes, no commerical business                                                                                  0           1     0     35
  yes, health treatment when possible, no commercial business                                                  0           0     0     25
  yes, no medical info, no commercial business                                                                 0           0     0      4
  yes, no questionnaires                                                                                       0           0     0      1
  yes, no tissue, no questionnaires, no health treatment, no medical info                                      0           0     0      0
  yes, no tissue, no questionnaires, no health treatment, no commercial business                               0           0     0      0
  yes, no medical info                                                                                         0           0     1      5
  yes, no questionnaires, no commercial business                                                               0           0     0      0
  yes, no questionnaires, no health treatment, no medical info                                                 0           0     0      1
  yes, no questionnaires, health treatment when possible, no commercial business                               0           0     0      0
  yes,  no health treatment, no medical info                                                                   0           0     0      5
  no, doesn't want to                                                                                          0           0     0      0
  no, unable to sign                                                                                           0           0     0      0
  no, no reaction                                                                                              0           0     0      0
  no, lost                                                                                                     0           0     0      0
  no, too old                                                                                                  0           0     0      0
  yes, no medical info, health treatment when possible                                                         0           0     0      0
  no (never asked for IC because there was no tissue)                                                          0           0     0      0
  yes, no medical info, no commercial business, health treatment when possible                                 0           0     0      2
  no, endpoint                                                                                                 0           0     0      0
  wil niets invullen, wel alles gebruiken                                                                      0           0     0      7
  second informed concents: yes, no commercial business                                                        0           0     0      2
  nooit geincludeerd                                                                                           0           0     0      0

AEGSselect <- subset(AEGS, 
                     informedconsent != "missing" &
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd")

AEGSselect.CEA <- subset(AEGS, !is.na(QC2018_FILTER) & QC2018_FILTER != "issue" & QC2018_FILTER != "family_discard" &
                     (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                     informedconsent != "missing" &
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd")

dim(AEGSselect)

[1] 3458 1136

table(AEGSselect$Artery_summary, AEGSselect$QC2018_FILTER)

                                                                                         
                                                                                          family_discard family_keep issue passed
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA              0           0     0      0
  carotid (left & right)                                                                              20          21    40   1819
  femoral/iliac (left, right or both sides)                                                            1           0     0     99
  other carotid arteries (common, external)                                                            0           0     0      9
  carotid bypass and injury (left, right or both sides)                                                0           0     0      1
  aneurysmata (carotid & femoral)                                                                      0           0     0      0
  aorta                                                                                                0           0     0      0
  other arteries (renal, popliteal, vertebral)                                                         0           0     0      1
  femoral bypass, angioseal and injury (left, right or both sides)                                     0           0     0      0

table(AEGSselect$Artery_summary, AEGSselect$CHIP)

                                                                                         
                                                                                          AffyAxiomCEU AffySNP5 IllGSA
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA            0        0      0
  carotid (left & right)                                                                           863      550    487
  femoral/iliac (left, right or both sides)                                                          0       72     28
  other carotid arteries (common, external)                                                          2        5      2
  carotid bypass and injury (left, right or both sides)                                              0        1      0
  aneurysmata (carotid & femoral)                                                                    0        0      0
  aorta                                                                                              0        0      0
  other arteries (renal, popliteal, vertebral)                                                       0        1      0
  femoral bypass, angioseal and injury (left, right or both sides)                                   0        0      0

table(AEGSselect$QC2018_FILTER, AEGSselect$CHIP)

                
                 AffyAxiomCEU AffySNP5 IllGSA
  family_discard           12        6      3
  family_keep               8        1     12
  issue                    37        1      2
  passed                  808      621    500

table(AEGSselect$QC2018_FILTER, AEGSselect$SAMPLE_TYPE)

                
                 EDTA blood plaque unknown
  family_discard         15      6       0
  family_keep            13      8       0
  issue                  26     14       0
  passed               1199    729       1

AEDB.temp <- subset(AEGSselect,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "QC2018_FILTER", "CHIP", "SAMPLE_TYPE"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
AEDB.temp$QC2018_FILTER <- to_factor(AEDB.temp$QC2018_FILTER)
AEDB.temp$CHIP <- to_factor(AEDB.temp$CHIP)
AEDB.temp$SAMPLE_TYPE <- to_factor(AEDB.temp$SAMPLE_TYPE)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

Athero-Express Genomics Study: baseline characteristics

Showing the baseline table of the Athero-Express Genomics Study.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEGSselect$GWAS <- to_factor(AEGSselect$GWAS)
AEGSselect$CHIP <- to_factor(AEGSselect$CHIP)
AEGSselect$PCA <- to_factor(AEGSselect$PCA)
AEGSselect$SAMPLE_TYPE <- to_factor(AEGSselect$SAMPLE_TYPE)
AEGSselect$informedconsent <- to_factor(AEGSselect$informedconsent)

AEGSselect.CEA$GWAS <- to_factor(AEGSselect.CEA$GWAS)
AEGSselect.CEA$CHIP <- to_factor(AEGSselect.CEA$CHIP)
AEGSselect.CEA$PCA <- to_factor(AEGSselect.CEA$PCA)
AEGSselect.CEA$SAMPLE_TYPE <- to_factor(AEGSselect.CEA$SAMPLE_TYPE)
AEGSselect.CEA$informedconsent <- to_factor(AEGSselect.CEA$informedconsent)


cat("===========================================================================================\n")

===========================================================================================

cat("CREATE BASELINE TABLE\n")

CREATE BASELINE TABLE

# Baseline table variables
basetable_vars = c("Hospital", 
                   "Age", "Gender", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO", 
                   "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
                   "DiabetesStatus", "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", 
                   "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin", "neutrophils", "Mast_cells_plaque", "vessel_density_averaged",
                   "IPH.bin", 
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index",
                   "SMC_rankNorm", "MAC_rankNorm", "Neutrophils_rankNorm", "MastCells_rankNorm", "VesselDensity_rankNorm",
                  "GWAS", "CHIP", "PCA",
                  "Plasma_PCSK9", "Plasma_PCSK9_rankNorm")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO", 
                  "SmokerCurrent", 
                  "DiabetesStatus", "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", 
                  "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index",
                  "GWAS", "CHIP", "PCA")

basetable_bin

 [1] "Gender"                      "KDOQI"                       "BMI_WHO"                     "SmokerCurrent"               "DiabetesStatus"             
 [6] "Hypertension.selfreport"     "Hypertension.selfreportdrug" "Hypertension.composite"      "Hypertension.drugs"          "Med.anticoagulants"         
[11] "Med.all.antiplatelet"        "Med.Statin.LLD"              "Stroke_Dx"                   "sympt"                       "Symptoms.5G"                
[16] "restenos"                    "EP_composite"                "Macrophages.bin"             "SMC.bin"                     "IPH.bin"                    
[21] "Calc.bin"                    "Collagen.bin"                "Fat.bin_10"                  "Fat.bin_40"                  "OverallPlaquePhenotype"     
[26] "Plaque_Vulnerability_Index"  "GWAS"                        "CHIP"                        "PCA"                        

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
basetable_con

 [1] "Hospital"                "Age"                     "TC_final"                "LDL_final"               "HDL_final"               "TG_final"               
 [7] "systolic"                "diastoli"                "GFR_MDRD"                "BMI"                     "eCigarettes"             "ePackYearsSmoking"      
[13] "EP_composite_time"       "macmean0"                "smcmean0"                "neutrophils"             "Mast_cells_plaque"       "vessel_density_averaged"
[19] "SMC_rankNorm"            "MAC_rankNorm"            "Neutrophils_rankNorm"    "MastCells_rankNorm"      "VesselDensity_rankNorm"  "Plasma_PCSK9"           
[25] "Plasma_PCSK9_rankNorm"  

All Athero-Express Genomics Study data (n = 2,011), compared to the remaining, _un_genotyped Athero-Express Biobank Study.

cat("\n===========================================================================================\n")
cat("DISPLAY BASELINE TABLE\n")

AEGSselect.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "GWAS",
                                         data = AEGSselect, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]

Baseline of the valid, CEA and genotyped data.

AEGSselect.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "Gender",
                                         data = AEGSselect.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]

Athero-Express Genomics Study: writing

Let’s save the baseline characteristics of the Athero-Express Genomics Study.

# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AEGS.BaselineTable.xlsx"), 
           AEGSselect.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "AEGS_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AEGS.CEA.BaselineTable.xlsx"), 
           AEGSselect.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "AEGS_Baseline_full")

SampleLists

Autosomal data.

We are ready to make a sampleList for use with the imputed data.

require(openxlsx)

temp <- subset(AEGS,
               GWAS == "genotyped",
               select = c("ID_1", "ID_2", "UPID", "STUDY_NUMBER", # ID_2 is the order of samples!
                          "QC2018_FINAL", "QC2018_FILTER", "OriginalOrder_postMichImp_QC",
                          "AEGS_type", "CHIP", "STUDY_TYPE", "SAMPLE_TYPE", "PCA",
                          "PC1", "PC2", "PC3", "PC4", "PC5",
                          "PC6", "PC7", "PC8", "PC9", "PC10",
                          "Sex", "Age", "ORyear", 
                          "Calc.bin", "Collagen.bin", 
                          "Fat.bin_10", "Fat.bin_40", "IPH.bin", 
                          "SMC_rankNorm", "MAC_rankNorm", "Neutrophils_rankNorm", "MastCells_rankNorm", "VesselDensity_rankNorm")) # Select some phenotype of interest
dim(temp)

[1] 2124   35

# Fix things
attach(temp)

temp[,"Calcification"] <- NA
temp$Calcification[Calc.bin == "no/minor"] <- "control"
temp$Calcification[Calc.bin == "moderate/heavy"] <- "case"

temp[,"Collagen"] <- NA
temp$Collagen[Collagen.bin == "no/minor"] <- "control"
temp$Collagen[Collagen.bin == "moderate/heavy"] <- "case"

temp[,"Fat10"] <- NA
temp$Fat10[Fat.bin_10 == "<10%"] <- "control"
temp$Fat10[Fat.bin_10 == ">10%"] <- "case"

temp[,"Fat40"] <- NA
temp$Fat40[Fat.bin_40 == "<40%"] <- "control"
temp$Fat40[Fat.bin_40 == ">40%"] <- "case"

temp[,"IPH"] <- NA
temp$IPH[IPH.bin == "no"] <- "control"
temp$IPH[IPH.bin == "yes"] <- "case"

detach(temp)

# Making selection variable
attach(temp)
temp[,"SELECTION"] <- "not_selected"
temp$SELECTION[(QC2018_FILTER=="passed" | QC2018_FILTER=="family_keep") & (STUDY_TYPE=="CEA" & PCA=="EUR")] <- "selected"
detach(temp)
table(temp$SELECTION, temp$QC2018_FILTER)

              
               family_discard family_keep issue passed
  not_selected             23           0    40    153
  selected                  0          21     0   1887

table(temp$SELECTION, temp$STUDY_TYPE)

              
                CEA  FEA Other
  not_selected   94  109    12
  selected     1908    0     0

table(temp$SELECTION, temp$PCA)

              
                EUR nonEUR
  not_selected  167     45
  selected     1908      0

AEGS123_sample.list <- temp[order(temp$OriginalOrder_postMichImp_QC),]

AEGS123_sample.list$missing <- 0

sample_file_aegs <- dplyr::select(AEGS123_sample.list,
                                  ID_1, ID_2, missing, # ID_2 is the order of samples - that way we always know what the order should be
                                  UPID, STUDY_NUMBER, 
                                  QC2018_FINAL, QC2018_FILTER, SELECTION,
                                  AEGS_type, CHIP, STUDY_TYPE, SAMPLE_TYPE, PCA,
                                  PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10,
                                  Sex, Age, ORyear, 
                                  Calcification, Collagen, 
                                  Fat10, Fat40, IPH, 
                                  SMC_rankNorm, MAC_rankNorm, Neutrophils_rankNorm, MastCells_rankNorm, VesselDensity_rankNorm)  %>%
  mutate_if(is.numeric, as.character) %>%
  mutate(SAMPLE_TYPE = gsub(' ', '_', SAMPLE_TYPE)) %>%
  add_row(.before = 1, 
          ID_1 = "0", ID_2 = "0", missing = "0", 
          UPID = "D", STUDY_NUMBER = "C",
          QC2018_FINAL = "D", QC2018_FILTER = "D", SELECTION = "D",
          AEGS_type = "D", CHIP = "D", STUDY_TYPE = "D", SAMPLE_TYPE = "D", PCA = "D",
          PC1 = "C", PC2 = "C", PC3 = "C", PC4 = "C", PC5 = "C", PC6 = "C", PC7 = "C", PC8 = "C", PC9 = "C", PC10 = "C",
          Sex = "D", Age = "C", ORyear = "C", 
          Calcification = "B", Collagen = "B", 
          Fat10 = "B", Fat40 = "B", IPH = "B", 
          SMC_rankNorm = "P", MAC_rankNorm = "P", Neutrophils_rankNorm = "P", MastCells_rankNorm = "P", VesselDensity_rankNorm = "P") %>% ## identifiers: index for these is 1, and all base variables have 0 as identifier
  print()

dim(sample_file_aegs)

[1] 2125   36

fwrite(sample_file_aegs,
       file = paste0(SNP_loc, "/",Today,".",PROJECTNAME,".AEGS123.sample"),
       na = "NA", sep = "\t", quote = FALSE,
       row.names = FALSE, col.names = TRUE,
       showProgress = TRUE, verbose = TRUE)

This installation of data.table has not been compiled with OpenMP support.
  omp_get_num_procs()            1
  R_DATATABLE_NUM_PROCS_PERCENT  unset (default 50)
  R_DATATABLE_NUM_THREADS        unset
  R_DATATABLE_THROTTLE           unset (default 1024)
  omp_get_thread_limit()         1
  omp_get_max_threads()          1
  OMP_THREAD_LIMIT               unset
  OMP_NUM_THREADS                unset
  RestoreAfterFork               true
  data.table is using 1 threads with throttle==1024. See ?setDTthreads.
No list columns are present. Setting sep2='' otherwise quote='auto' would quote fields containing sep2.
Column writers: 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 
args.doRowNames=0 args.rowNames=0 doQuote=0 args.nrow=2125 args.ncol=36 eolLen=1
maxLineLen=845. Found in 0.000s
Writing bom (false), yaml (0 characters) and column names (true) ... done in 0.012s
Writing 2125 rows in 1 batches of 2125 rows (each buffer size 8MB, showProgress=1, nth=1)

require(DT)
DT::datatable(sample_file_aegs, caption = "AEGS: final sample list of genotyped AE patients after quality control.", rownames = FALSE)

It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlIt seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.html

rm(temp, temp2, temp3)

object 'temp2' not foundobject 'temp3' not found

X-chromosome data

The X-chromosome data is taken from previously imputed data based on 1000G phase 3 (version 5) and GoNL5. For some reason, imputing on the Michigan Imputation Server was not successful (ACTION point).

Here we load in the sample files for the three datasets of the X chromosomal data. We should:

• filter out samples that did not pass quality control, ending up with 2,124 sample
• re-order the data to fit the other autosomal data.

AEGS123_chrX <- fread(paste0(MICHIMP_loc, "/_chr23_1kg_gonl5/aegs.raw.1kg_gonl5.chr23.mappings.txt"))
names(AEGS123_chrX)[names(AEGS123_chrX) == "ID_1"] <- "SampleID_postImpChrX"

AEGS123_AllChr <- merge(AEGS123_chrX, sample_file_aegs, by.x = "SampleID_postMichImp", by.y = "ID_1", 
                        all.x = TRUE, 
                        sort = FALSE)

names(AEGS123_AllChr)[names(AEGS123_AllChr) == "ID_2.y"] <- "ID_2"
names(AEGS123_AllChr)[names(AEGS123_AllChr) == "STUDY_TYPE.y"] <- "STUDY_TYPE"
names(AEGS123_AllChr)[names(AEGS123_AllChr) == "SampleID_postMichImp"] <- "ID_1"
AEGS123_AllChr$missing.x <- NULL
AEGS123_AllChr$missing.y <- NULL
AEGS123_AllChr$STUDY_TYPE.x <- NULL
AEGS123_AllChr$ID_2.x <- NULL

dim(AEGS123_AllChr)

[1] 2176   41

str(AEGS123_AllChr)

Classes ‘data.table’ and 'data.frame':  2176 obs. of  41 variables:
 $ ID_1                  : chr  "0" "UPID00126-UPID00126-A318-0034" "UPID01799-UPID01799-A318-0492" "UPID01890-UPID01890-A318-0151" ...
 $ SampleID_postImpChrX  : chr  "0" "UPID00126-UPID00126-A318-0034" "UPID01799-UPID01799-A318-0492" "UPID01890-UPID01890-A318-0151" ...
 $ MappingID             : chr  "0" "UPID00126-UPID00126-A318-0034" "UPID01799-UPID01799-A318-0492" "UPID01890-UPID01890-A318-0151" ...
 $ FID_forQC             : chr  "0" "UPID00126" "UPID01799" "UPID01890" ...
 $ IID_forQC             : chr  "0" "UPID00126-UPID00126-A318-0034" "UPID01799-UPID01799-A318-0492" "UPID01890-UPID01890-A318-0151" ...
 $ SampleID_postQC       : chr  "0" "UPID00126-UPID00126-A318-0034" "UPID01799-UPID01799-A318-0492" "UPID01890-UPID01890-A318-0151" ...
 $ ChrX_Order            : int  0 1 2 NA 3 4 5 6 NA 7 ...
 $ ID_2                  : chr  "0" "22" "325" NA ...
 $ UPID                  : chr  "D" NA NA NA ...
 $ STUDY_NUMBER          : chr  "C" "901" "1164" NA ...
 $ QC2018_FINAL          : chr  "D" NA NA NA ...
 $ QC2018_FILTER         : chr  "D" "passed" "passed" NA ...
 $ SELECTION             : chr  "D" "selected" "selected" NA ...
 $ AEGS_type             : chr  "D" "AEGS1" "AEGS1" NA ...
 $ CHIP                  : chr  "D" "AffySNP5" "AffySNP5" NA ...
 $ STUDY_TYPE            : chr  "D" "CEA" "CEA" NA ...
 $ SAMPLE_TYPE           : chr  "D" "plaque" "plaque" NA ...
 $ PCA                   : chr  "D" "EUR" "EUR" NA ...
 $ PC1                   : chr  "C" "-0.0316" "-0.0131" NA ...
 $ PC2                   : chr  "C" "0.0161" "-0.0069" NA ...
 $ PC3                   : chr  "C" "-0.01" "0.0383" NA ...
 $ PC4                   : chr  "C" "-0.0131" "-0.005" NA ...
 $ PC5                   : chr  "C" "0.0059" "0.0078" NA ...
 $ PC6                   : chr  "C" "-0.0283" "0.0243" NA ...
 $ PC7                   : chr  "C" "-2e-04" "-0.018" NA ...
 $ PC8                   : chr  "C" "0.0033" "0.0022" NA ...
 $ PC9                   : chr  "C" "-0.0317" "-0.0154" NA ...
 $ PC10                  : chr  "C" "-0.0054" "0.007" NA ...
 $ Sex                   : chr  "D" "F" "M" NA ...
 $ Age                   : chr  "C" NA NA NA ...
 $ ORyear                : chr  "C" NA NA NA ...
 $ Calcification         : chr  "B" NA NA NA ...
 $ Collagen              : chr  "B" NA NA NA ...
 $ Fat10                 : chr  "B" NA NA NA ...
 $ Fat40                 : chr  "B" NA NA NA ...
 $ IPH                   : chr  "B" NA NA NA ...
 $ SMC_rankNorm          : chr  "P" NA NA NA ...
 $ MAC_rankNorm          : chr  "P" NA NA NA ...
 $ Neutrophils_rankNorm  : chr  "P" NA NA NA ...
 $ MastCells_rankNorm    : chr  "P" NA NA NA ...
 $ VesselDensity_rankNorm: chr  "P" NA NA NA ...
 - attr(*, ".internal.selfref")=<externalptr> 

This seems fine, let’s filter; we can use this file to filter the genetic data. And we create another file to re-order the data.

AEGS123_AllChrQC <- subset(AEGS123_AllChr,
               !is.na(QC2018_FILTER),
               select = c("ID_1", "ID_2", "UPID", "STUDY_NUMBER", "SampleID_postImpChrX",
                          "QC2018_FINAL", "QC2018_FILTER", "SELECTION",
                          "AEGS_type", "CHIP", "STUDY_TYPE", "SAMPLE_TYPE",
                          "PC1", "PC2", "PC3", "PC4", "PC5",
                          "PC6", "PC7", "PC8", "PC9", "PC10",
                          "Sex", "Age", "ORyear", 
                          "Calcification", "Collagen", 
                          "Fat10", "Fat40", "IPH", 
                          "SMC_rankNorm", "MAC_rankNorm", "Neutrophils_rankNorm", "MastCells_rankNorm", "VesselDensity_rankNorm"))

AEGS123_AllChrQC_reorder <-AEGS123_AllChrQC[order(AEGS123_AllChrQC$ID_2),] # remember: ID_2 is the order of samples

AEGS123_AllChrQC_filtered <- subset(AEGS123_AllChr,
               is.na(QC2018_FILTER),
               select = c("ID_1", "ID_2", "UPID", "STUDY_NUMBER", "SampleID_postImpChrX",
                          "QC2018_FINAL", "QC2018_FILTER", "SELECTION",
                          "AEGS_type", "CHIP", "STUDY_TYPE", "SAMPLE_TYPE",
                          "PC1", "PC2", "PC3", "PC4", "PC5",
                          "PC6", "PC7", "PC8", "PC9", "PC10",
                          "Sex", "Age", "ORyear", 
                          "Calcification", "Collagen", 
                          "Fat10", "Fat40", "IPH", 
                          "SMC_rankNorm", "MAC_rankNorm", "Neutrophils_rankNorm", "MastCells_rankNorm", "VesselDensity_rankNorm"))

fwrite(AEGS123_AllChrQC_reorder,
       file = paste0(SNP_loc, "/",Today,".",PROJECTNAME,".AEGS123.chrX.sample"),
       na = "NA", sep = "\t", quote = FALSE,
       row.names = FALSE, col.names = TRUE,
       showProgress = TRUE, verbose = TRUE)

This installation of data.table has not been compiled with OpenMP support.
  omp_get_num_procs()            1
  R_DATATABLE_NUM_PROCS_PERCENT  unset (default 50)
  R_DATATABLE_NUM_THREADS        unset
  R_DATATABLE_THROTTLE           unset (default 1024)
  omp_get_thread_limit()         1
  omp_get_max_threads()          1
  OMP_THREAD_LIMIT               unset
  OMP_NUM_THREADS                unset
  RestoreAfterFork               true
  data.table is using 1 threads with throttle==1024. See ?setDTthreads.
No list columns are present. Setting sep2='' otherwise quote='auto' would quote fields containing sep2.
Column writers: 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 
args.doRowNames=0 args.rowNames=0 doQuote=0 args.nrow=2013 args.ncol=35 eolLen=1
maxLineLen=912. Found in 0.000s
Writing bom (false), yaml (0 characters) and column names (true) ... done in 0.008s
Writing 2013 rows in 1 batches of 2013 rows (each buffer size 8MB, showProgress=1, nth=1)

require(DT)
DT::datatable(AEGS123_AllChrQC, caption = "AEGS: final sample list of genotyped AE patients after quality control (chromosome X).", rownames = FALSE)

It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlIt seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.html

GWASToolKit preparation

VariantLists

Here we create a variantlist.txt file used by GWASToolKit for analysis.

variant_list

temp <- subset(variant_list, select = c("variantid", "chr", "pos"))

fwrite(temp,
       file = paste0(SNP_loc, "/variantlist.txt"),
       na = "NA", sep = "\t", quote = FALSE,
       row.names = FALSE, col.names = FALSE,
       showProgress = TRUE, verbose = TRUE)

This installation of data.table has not been compiled with OpenMP support.
  omp_get_num_procs()            1
  R_DATATABLE_NUM_PROCS_PERCENT  unset (default 50)
  R_DATATABLE_NUM_THREADS        unset
  R_DATATABLE_THROTTLE           unset (default 1024)
  omp_get_thread_limit()         1
  omp_get_max_threads()          1
  OMP_THREAD_LIMIT               unset
  OMP_NUM_THREADS                unset
  RestoreAfterFork               true
  data.table is using 1 threads with throttle==1024. See ?setDTthreads.
No list columns are present. Setting sep2='' otherwise quote='auto' would quote fields containing sep2.
Column writers: 12 5 5 
args.doRowNames=0 args.rowNames=0 doQuote=0 args.nrow=898 args.ncol=3 eolLen=1
maxLineLen=144. Found in 0.000s
Writing bom (false), yaml (0 characters) and column names (false) ... done in 0.025s
Writing 898 rows in 1 batches of 898 rows (each buffer size 8MB, showProgress=1, nth=1)

rm(temp)

Covariates

Here we create a covariates.txt file used by GWASToolKit for analysis.

library(tidyverse)
# for 'overall' analyses
c("Age Sex PC1 PC2 CHIP ORyear") %>% write_lines(paste0(SNP_loc, "/covariates.txt"))

# for sex-specific analyses
c("Age PC1 PC2 CHIP ORyear") %>% write_lines(paste0(SNP_loc, "/covariates.sex.txt"))

Phenotypes

Here we create a phenotypes.txt file used by GWASToolKit for analysis.

library(tidyverse)
c("Calcification", "Collagen", "Fat10", "Fat40", "IPH", "SMC_rankNorm", "MAC_rankNorm", "Neutrophils_rankNorm", "MastCells_rankNorm", "VesselDensity_rankNorm") %>% write_lines(paste0(SNP_loc, "/phenotypes.txt"))

Session information

---

Version:      v1.0.3
Last update:  2021-03-11
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to get some Athero-Express Biobank Study baseline characteristics.
Minimum requirements: R version 3.4.3 (2017-06-30) -- 'Single Candle', Mac OS X El Capitan

Changes log
* v1.0.3 Update to the gene list.
* v1.0.2 Updated baseline characteristics information. Update GWASToolKit preparation. Added more candidate SNPs to look at. Updated some variable names. 
* v1.0.1 Add an additional phenotype.
* v1.0.0 Initial version. Fixed baseline table, added codes, and results. Created sample-files.

---

sessionInfo()

R version 4.0.4 (2021-02-15)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] grid      tools     stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gridExtra_2.3        plyr_1.8.6           labelled_2.7.0       UpSetR_1.4.0         ggpubr_0.4.0         forestplot_1.10.1    checkmate_2.0.0      magrittr_2.0.1      
 [9] pheatmap_1.0.12      devtools_2.3.2       usethis_2.0.1        BlandAltmanLeh_0.3.1 tableone_0.12.0      haven_2.3.1          eeptools_1.2.4       DT_0.17             
[17] knitr_1.31           forcats_0.5.1        stringr_1.4.0        purrr_0.3.4          tibble_3.1.0         ggplot2_3.3.3        tidyverse_1.3.0      data.table_1.14.0   
[25] naniar_0.6.0         tidyr_1.1.3          dplyr_1.0.5          optparse_1.6.6       readr_1.4.0          openxlsx_4.2.3      

loaded via a namespace (and not attached):
  [1] estimability_1.3     scattermore_0.7      coda_0.19-4          SeuratObject_4.0.0   visdat_0.5.3         irlba_2.3.3          multcomp_1.4-16      rpart_4.1-15        
  [9] generics_0.1.0       callr_3.5.1          cowplot_1.1.1        TH.data_1.0-10       RANN_2.6.1           future_1.21.0        spatstat.data_2.0-0  xml2_1.3.2          
 [17] lubridate_1.7.10     httpuv_1.5.5         assertthat_0.2.1     xfun_0.21            hms_1.0.0            jquerylib_0.1.3      evaluate_0.14        promises_1.2.0.1    
 [25] fansi_0.4.2          dbplyr_2.1.0         readxl_1.3.1         igraph_1.2.6         DBI_1.1.1            htmlwidgets_1.5.3    ellipsis_0.3.1       crosstalk_1.1.1     
 [33] backports_1.2.1      insight_0.13.1       survey_4.0           deldir_0.2-10        vctrs_0.3.6          remotes_2.2.0        ROCR_1.0-11          sjlabelled_1.1.7    
 [41] abind_1.4-5          cachem_1.0.4         withr_2.4.1          emmeans_1.5.4        vcd_1.4-8            sctransform_0.3.2    prettyunits_1.1.1    getopt_1.20.3       
 [49] goftest_1.2-2        cluster_2.1.1        lazyeval_0.2.2       crayon_1.4.1         labeling_0.4.2       pkgconfig_2.0.3      nlme_3.1-152         pkgload_1.2.0       
 [57] nnet_7.3-15          rlang_0.4.10         globals_0.14.0       lifecycle_1.0.0      miniUI_0.1.1.1       sandwich_3.0-0       modelr_0.1.8         cellranger_1.1.0    
 [65] rprojroot_2.0.2      polyclip_1.10-0      matrixStats_0.58.0   lmtest_0.9-38        Matrix_1.3-2         carData_3.0-4        boot_1.3-27          zoo_1.8-8           
 [73] reprex_1.0.0         base64enc_0.1-3      ggridges_0.5.3       processx_3.4.5       png_0.1-7            viridisLite_0.3.0    parameters_0.12.0    KernSmooth_2.23-18  
 [81] pander_0.6.3         maptools_1.0-2       arm_1.11-2           parallelly_1.23.0    jpeg_0.1-8.1         rstatix_0.7.0        ggeffects_1.0.1      ggsignif_0.6.1      
 [89] scales_1.1.1         memoise_2.0.0        ica_1.0-2            compiler_4.0.4       tinytex_0.30         RColorBrewer_1.1-2   lme4_1.1-26          fitdistrplus_1.1-3  
 [97] cli_2.3.1            listenv_0.8.0        patchwork_1.1.0.9000 pbapply_1.4-3        ps_1.6.0             htmlTable_2.1.0      Formula_1.2-4        MASS_7.3-53.1       
[105] mgcv_1.8-34          tidyselect_1.1.0     stringi_1.5.3        mitools_2.4          yaml_2.2.1           latticeExtra_0.6-29  ggrepel_0.9.1        sass_0.3.1          
[113] future.apply_1.7.0   parallel_4.0.4       rio_0.5.26           rstudioapi_0.13      foreign_0.8-81       farver_2.1.0         Rtsne_0.15           sjPlot_2.8.7        
[121] digest_0.6.27        BiocManager_1.30.10  shiny_1.6.0          Rcpp_1.0.6           car_3.0-10           broom_0.7.5          performance_0.7.0    later_1.1.0.1       
[129] RcppAnnoy_0.0.18     httr_1.4.2           effectsize_0.4.3     sjstats_0.18.1       colorspace_2.0-0     rvest_0.3.6          fs_1.5.0             tensor_1.5          
[137] reticulate_1.18      splines_4.0.4        uwot_0.1.10          statmod_1.4.35       spatstat.utils_2.0-0 sp_1.4-5             plotly_4.9.3         sessioninfo_1.1.1   
[145] xtable_1.8-4         jsonlite_1.7.2       nloptr_1.2.2.2       spatstat_1.64-1      testthat_3.0.2       R6_2.5.0             Hmisc_4.5-0          pillar_1.5.1        
[153] htmltools_0.5.1.1    mime_0.10            glue_1.4.2           fastmap_1.1.0        minqa_1.2.4          class_7.3-18         codetools_0.2-18     pkgbuild_1.2.0      
[161] mvtnorm_1.1-1        utf8_1.1.4           lattice_0.20-41      bslib_0.2.4          curl_4.3             leiden_0.3.7         zip_2.1.1            survival_3.2-7      
[169] rmarkdown_2.7        desc_1.3.0           munsell_0.5.0        e1071_1.7-4          sjmisc_2.8.6         reshape2_1.4.4       gtable_0.3.0         bayestestR_0.8.2    
[177] Seurat_4.0.0        

© 1979-2021 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | swvanderlaan.github.io.